US 5,412,752 AGrant
Expandable Fuzzy Microcontroller Core
Issue Date:1995-05-02
•12 Claims
•17 Drawing Sheets
Abstract
An arrangement (apparatus and method) using fuzzy logic for controlling a plurality of devices in response to inputs. The input signals are sequentially selected and supplied to a fuzzy input processor. The crisp input signal is fuzzified by selected center and width data corresponding to a selected membership function. Each crisp input is fuzzified by determining the distance of the crisp input from the center of the membership function and linearly complementing the result with respect to the width of the membership function, thereby eliminating the necessity for determining the shape of the membership function. The center and width may be fixed or dynamic values. The fuzzified input signals are successively compared in a min-max comparator which determine a minimum rule term for each rule processed. The minimum rule term is compared with a previously stored minimum rule term to determine a maximum among the minimum rule terms. The fuzzy input processor outputs a clock signal whenever a new maximum among the minimal rule terms is determined, and stores the new maximum value. The arrangement also incudes a fuzzy output processor which executes the rule identified by the fuzzy input processor as the optimum rule. A fuzzy memory interface controls the transfer of data for the fuzzy input processor and the fuzzy output processor, thereby optimizing parameter storage.
Metadata
Assignee
- American Neurologix, Inc.
Inventors
- Paul M. Basehore
- Charles D. Watson
Application Information
Application Number:US 041235&
Filing Date:1993-03-31
Priority Date:1991-06-12
Art Unit:238
Classifications
IPC:
G06F 1500
Field of Search:
3953643;11;51;61;900148
Patent Drawings (17 sheets)
Description
Background of the Invention
1. Field of the Invention
The present invention relates to controllers and rule-based expert control systems using fuzzy logic and parallel processing for controlling devices.
2. Description of the Related Art
Control systems and computer-controlled electronic devices have historically been controlled by digital control systems. Such control systems use bi-state digital logic which requires a value of either "TRUE" or "FALSE" so that approximations are often required of real-world control problems. For example, an input/output relationship y=f(x) would be specified either as a mathematical function or as a series of points using, for example, a look-up table: the former use of a mathematical function may require complex mathematics to accurately represent real-world control problems; further, the latter use of a look-up table, such as a ROM, introduces problems such as large memory requirements for adequate approximation, the concomitant addressing function associated with large memories, as well as interpolation problems.
For example, FIG. 1A shows an exemplary nonlinear sigmoidal function y=f(x). If digital logic was used to characterize the function y=f(x), it would be necessary to approximate the function shown in FIG. 1A by using discrete values, y.sub.i =a.sub.i. x.sub.i (i=1, 2, . . . , n), as shown in FIG. 1B. Since the number n of "crisp" values is limited, there inherently exists an interpolation error for values of x between x.sub.i and x.sub.i+1. The term "crisp" refers to an input having a single discrete value. In addition, it becomes impractical to write a rule for every input combination where there exists a large number of inputs whose values can cover a wide input range.
An alternative approach to control theory, known as "fuzzy logic", was developed by L. Zadeh in 1963. Rather than evaluating the two values "TRUE" and "FALSE" as in digital logic, fuzzy terms admit to degrees of membership in multiple sets so that fuzzy rules may have a continuous, rather than stepwise, range of truth of possibility. For example, in applying fuzzy logic, a person need not strictly be included or excluded as a member from a set of "tall persons"; rather, to the extent a person may be "tall" to a greater or lesser degree, the member is assigned to the set with a degree of membership between the values of "1" and "0".
FIG. 1C illustrates the principle of fuzzy logic in evaluating the function illustrated in FIG. 1A. The function f(x) is approximated by a plurality of fuzzy sets 10 which overlap. Rather than approximating a continuous value x by a discrete value x.sub.i, fuzzy logic determines for a given value x whether the value x is nearest to the center of a fuzzy set 10. If an x value is equidistant from two or more fuzzy sets, the resultant y value can be made proportional to the output values suggested by all the fuzzy sets of which the value x is a member. Thus, a fuzzy number may be two dimensional, having assigned fuzzy sets and corresponding membership values.
Since fuzzy logic can operate within the relative imprecision of the real-world environment, the advantages of fuzzy logic and fuzzy set theory have become apparent in numerous areas, such as robotics, natural language recognition, the automobile and aircraft industry, artificial intelligence, etc. Exemplary publications describing the theory and applications of fuzzy logic include Devi, B. B. et al., Estimation of Fuzzy Memberships from Histograms, Information Sciences, vol. 35, 1985, pp. 43-59; Braae, M. et al., Theoretical and Linguistic Aspects of the Fuzzy Logic Controller, Automatica, vol. 15, issue 5, 1979, pp. 553-77; Ralston, P. A. S., Fuzzy Logic Control of Machining, Manufacturing Review, vol. 3, no. 3, September 1990, pp. 147-154; Schwartz, Tom J., Fuzzy Tools for Expert Systems, AI Expert, February 1991, pp. 34-41; Kohoutek, H. J., Practice of Approximate Reasoning, Proceedings of Manufacturing International '90 (1990: Atlanta, Ga.) Vol. V: Design, Reliability, and Education of Manufacturability; Lee, Chuen C., Fuzzy Logic in Control Systems: Fuzzy Logic Controller--Parts I & II, IEEE Transactions on Systems, Man and Cybernetics vol. 20, issue 2, March/April 1990, pp. 404-418, 419-435; Mamdani, E. H., Application of Fuzzy Algorithms for Control of Simple Dynamic Plant, Proceedings of the IEEE, vol. 121, issue 12, December 1974, pp. 1585-1588; Tang, K. L. et al., Comparing Fuzzy Logic with Classical Controller Designs, IEEE Transactions on Systems, Man and Cybernetics, vol. 17, issue 6, November/December 1987, pp. 1085-1087; Zadeh, L. A., Fuzzy Sets, Information and Control, vol. 8, 1965, pp. 338-353; Zadeh, L. A., Outline of a New Approach to the Analysis of Complex Systems and Decision Processes, IEEE Transactions on Systems, Man and Cybernetics, vol. 3, issue 1, January 1973, pp. 28-44; and Ruspini, Enrique H., On the Semantics of Fuzzy Logic, International Journal of Approximate Reasoning 1991, vol. 5, pp. 45-86. These publications, which describe in detail the theory of fuzzy logic, are incorporated herein by reference.
The implementation of fuzzy logic for a controller has been suggested by Ralston et al. (1990). As shown in FIG. 2, a "universe of discourse" (e.g., a dimension of continuous values) is defined in step 20 for input and control variables. Membership functions are then defined in step 22 which determine the way observations of the variables are expressed as fuzzy sets. In other words, the fuzzy sets, which are subsets of the universe of discourse, are defined by their respective membership functions. A rule base is then formulated or constructed in step 24 to supply the logic that relates observations applied to the controller input to the required fuzzy control statements; a rule may have the format "IF (Alpha IS SMALL) AND (Beta IS MEDIUM) THEN Gamma IS BIG". A computational unit is then designed in step 26 that uses the rule base to logically obtain control statements from the fuzzy inputs; the computational unit will provide fuzzy outputs in response to the process observations, e.g., the fuzzy inputs. Finally, an action interface is defined in step 28 which will provide rules to translate fuzzy control statements into discrete, crisp values for the input variables applied to the input of the controlled device or process.
The implementation of fuzzy logic in control systems, while promising, has encountered numerous problems. For example, the construction of membership functions may be relatively difficult: the optimum membership function is related to sensor characteristics, control responses, and other dynamic factors. Thus, the determination of the optimum membership function in some cases may only be accomplished empirically. This difficulty in determining the membership function translates into the problem of properly classifying an input into the corresponding fuzzy set.
An additional problem of current fuzzy control systems is the increase in cost due to attempts to increase processing speed.
Highly parallel architectures which are faster than the classic von-Neumann architecture have recently been proposed by Lippman (1987). While these neural net classifiers are faster than the classic von-Neumann machine, the relatively large number of parallel connections in the neural net increases the size of an IC chip, and thus increases the overall cost of the system.
The fuzzy logic systems to date have only been implemented having a relatively small number of inputs. A fuzzy logic microcontroller must have the capacity to handle a large number of inputs, function in accordance with a large number of rules and provide an adequate number of outputs.
Further, more advanced applications of fuzzy logic may require a fuzzy logic microcontroller capable of processing complex membership functions that have varying parameters. For example, a fuzzy logic application may have a fuzzy set having a membership function that varies with respect to time or a given input parameter. Thus, unless the membership function can be defined on the basis of the varying parameter, the definition of the membership function would need to be continually updated during processing, thereby adding substantial complexity to the fuzzifying process. In other words, any adaptive fuzzy logic system would require expert systems to modify the characteristics of the fuzzy logic rules and the topology of the fuzzy sets. Such a system would be costly and complex because of the relatively large memory and processing requirements. Since the proposed system would be trained by prior experiences and not logically trained, an initial training interval would be necessary before the proposed system could be usable. The proposed system would also suffer the risk of following into a local minimum during training, thereby reducing the system's overall efficiency.
Therefore, it would be desirable to provide a simple implementation of fuzzy sets that have membership functions defined on the basis of varying parameters in order to provide a fully adaptive fuzzy logic system.
The fuzzy microcontroller in application Ser. No. 07/712,871 fuzzified a number of inputs and determined an optimum rule from a set of rules corresponding to an output. The optimum rule had a corresponding action value which was either output directly in an immediate mode or added as an offset to the previous output value in an accumulate mode: this step was known as defuzzifying the output signal. However, different fuzzy logic applications may have different desired outputs. For example, the application Ser. No. 07/712,871 disclosed an application combining a plurality of fuzzy microcontrollers by coupling the output of one microcontroller to the input of a second microcontroller; it would be desirable to directly output the fuzzy results of the first microcontroller as raw data for the second microcontroller. Further, it would be desirable to provide an output mode which outputs a result based upon the average of all the rules considered valid during the determination of the optimum rule.
Further, the fuzzy microcontroller of application Ser. No. 07/712,871 cycled through a predetermined number of inputs during the fuzzification of the inputs using a predetermined number of fuzzifiers and a predetermined number of rules, regardless of the needs of the application. Consequently, the fuzzy microcontroller of application Ser. No. 07/712,871 would have the same throughput even if only one fuzzifier or one rule was being processed. It would be desirable to provide a system that provided maximum design flexibility to ensure optimum performance and minimal cost for a designer's specific application.
Finally, the fuzzy microcontrollers of applications Ser. No. 07/712,871 and 07/893,093 had specifically defined membership functions and rules. Thus, a separate memory location was needed for each membership function or rule, regardless of whether there were common parameters for the membership function or rules. It would be desirable to provide a system enabling a more flexible definition structure for membership functions and rules to maximize memory storage efficiency of parameter data.
Summary of the Invention
It is an object of the present invention to provide a low cost fuzzy logic microcontroller for efficient performance in cost-critical control systems.
It is a further object of the present invention to provide a fuzzy logic microcontroller which is easily implemented for desired control applications.
It is still a further object of the present invention to provide a fuzzy logic microcontroller which has an efficient processing capacity to generate control signals in response to a large number of inputs.
It is still another object of the present invention to provide a fuzzy logic microcontroller which has flexible processing capabilities for complex fuzzy logic applications, such as adaptive fuzzy logic systems.
In order to achieve these and other objects of the present invention, a fuzzy logic processor system, also referred to as fuzzy microcontroller, includes several advantageous features which together provide efficient processing from a large number of inputs. The fuzzy logic processor system includes a fuzzy input processor, a fuzzy output processor and a fuzzy memory interface. Each of these elements of the fuzzy logic processor system are designed for maximum flexibility and may be used separately with other digital systems as cores for Application Specific Integrated Circuit (ASIC) designs. Since each of these elements are available as ASIC cores, the customization of fuzzy circuits becomes easier and more affordable.
The fuzzy input processor, also known as a fuzzy processor, directly receives input data and membership function parameter data, namely membership function center, width and type. The input data may be supplied directly or from a multiplexer-based system, and may represent any digital source, such as sensor data, memory data, loop-back data, or any combination thereof. The fuzzy input processor successively determines a membership value for each data input. After fuzzification, the fuzzified input signals are processed in accordance with predetermined fuzzy logic rules established in accordance with the designer's specific application parameters in order to determine the optimum rule for execution. Once the optimum rule is identified, the fuzzy logic processor system executes the optimum rule.
The present invention provides a fuzzification method which provides a simple technique for determining the similarity of the crisp input (e.g., the discrete demultiplexed signal undergoing fuzzification) to a fuzzy set. The degree of similarity between the crisp input and the fuzzy set is defined by a membership function .mu.. The fuzzification method according to the present invention disregards the shape of the membership function .mu. and instead determines the distance of the crisp input from a defined center of the membership function .mu.; the distance of the crisp input is then linearly complemented with respect to the width of the membership function .mu. so that the resulting similarity signal is a maximum when the distance is a minimum, and, conversely, a minimum when the distance is a maximum (e.g., when the crisp input exceeds the width of the membership function .mu.).
Since the shape of the membership function is not used in the similarity determination, the designer of a control system no longer needs to be concerned with the optimum membership function shape or its effects due to sensor characteristics, control responses, or other dynamic factors. As a result, the fuzzification process according to the present invention is simpler to implement while retaining flexibility for variations in control systems.
The fuzzification method according to the present invention is implemented by the fuzzy input processor. The fuzzy input processor fuzzifies the input in accordance with the supplied membership function parameter data to obtain a fuzzified input signal.
A particular advantage of the present invention is that the membership function parameter data is directly supplied to the fuzzy input processor. As a result, a designer may define a predetermined center or may assign an input source to be a center value. The assignment of an input source to be a center value causes the corresponding membership function to have a variable, or "floating" center. Since an input may either be an external signal, a feedback signal or an internal timing signal, the use of such floating centers are particularly effective in applications requiring time-variable membership functions, as well as applications comparing two changing inputs to determine a differential or to eliminate a noise component. Similarly, the membership function width data can be supplied from memory as predetermined data or from any of the dynamic sources described above. Thus, the present invention provides the ability to implement dynamic width fuzzifiers. As a result, the present invention provides a self-adapting fuzzy logic system that does not require any modification by expert systems.
The fuzzy input processor includes a Min/Max comparator for successively comparing the fuzzified input signals to each other in accordance with predetermined fuzzy logic rules established in accordance with control system output parameters. In other words, an output of the fuzzy logic processor system will be determined on the basis of its predetermined rules and the fuzzified input signals. Each output has a set of rules and each rule has a set of rule terms. Thus, the Min/Max comparator sequentially determines the fuzzy input signals which represent the minimum value term in the rule currently processed. The Min/Max comparator then successively compares each of the minimum value terms for each rule and identifies which rule provides the optimum output given the fuzzified inputs. The use of the Min/Max comparator provides minimum use of silicon on a semiconductor chip, resulting in a lower cost for the semiconductor chip.
The fuzzy input processor outputs the minimum term for each rule as the processing for each rule is completed. Three strobe signals enable a user to identify the data output as a minimum rule term for a corresponding rule or as a max-of-min term for all rules that have been processed. Thus, a user can monitor the strobe signals to determine whether the minimum rule term is a relative maximum for those rules processed.
The Max-of-Min value output from the fuzzy input processor represents the actual minimum value of the winning rule from the minimum comparator, which can serve as raw data when a plurality of fuzzy input processors are connected, thereby improving processing efficiency or troubleshooting efforts.
The fuzzy logic processor system also includes a fuzzy output processor which outputs the desired output data in accordance with an output mode of the corresponding winning rule. In addition to the max-of-min value output by the fuzzy input processor, the fuzzy output processor provides three preferred output modes: Immediate, Accumulate, and Weighted Average. For example, in the Accumulate mode, the action value corresponding to the winning rule adds an offset to the existing output data, and outputs the resulting output signal to a device to be controlled. The Weighted Average mode, also known as a centroid mode, effectively offers a fuzzy output by modifying the output by the average of all actions of all corresponding rules where a membership function was considered valid. Thus, the output is a more accurate response to the input data.
The fuzzy logic processor system also includes a fuzzy memory interface capable of providing addressing control in accordance with the designer's specific application requirements. Specifically, the fuzzy memory interface controls the addressing and selection of data values from external memories or data sources to provide the necessary input data, fuzzy set parameter data or rule term data. As a result, a system designer can develop a customized system optimized for his application with minimal use of unnecessary components.
Thus, the present invention provides a unique fuzzy microcontroller which enables rapid processing of a plurality of inputs using fuzzy logic and which is relatively simple and inexpensive to implement for fuzzy control systems.
As a result, the present invention provides a new and inexpensive system of control for applications such as smart appliances, pattern matching, sequences, state machines and timers, automotive applications, robotics, approximate reasoning, or expert systems.
The features of the present invention will become more readily apparent from the below detailed description and the accompanying drawings.
Brief Description of the Drawings
Reference is made to the attached drawings, wherein elements having the same reference numeral designations represent like elements throughout, and wherein:
FIGS. 1A, 1B and 1C illustrate the logical distinctions between digital logic and fuzzy logic;
FIG. 2 discloses a conventional method for fuzzy logic in a controller;
FIG. 3 discloses a block diagram of the fuzzy microcontroller according to a preferred embodiment of the present invention;
FIG. 4 illustrates a relationship between two fuzzy sets and a crisp input;
FIGS. 5A-5F illustrate exemplary relationships between a fuzzy set and a range of inputs on the basis of the membership function of the fuzzy set according to the fuzzifying method of the present invention;
FIG. 6 discloses a block diagram of the fuzzy input processor of FIG. 3;
FIGS. 7A and 7B discloses a preferred schematic diagram of the distance measurement circuit of FIG. 6;
FIG. 8A and 8B discloses the Max/Min comparator of FIG. 6;
FIG. 9 discloses a timing diagram of the fuzzy input processor of FIG. 6;
FIG. 10 discloses a block diagram of the fuzzy output processor of FIG. 3;
FIG. 11 discloses a timing diagram of the fuzzy output processor of FIG. 10;
FIG. 12A discloses a block diagram of the fuzzy memory interface of FIG. 3;
FIG. 12B discloses an exemplary memory structure for rule terms processed according to the present invention;
FIG. 13 discloses a timing diagram of the fuzzy memory interface of FIG. 12;
FIGS. 14A, 14B and 14C illustrate exemplary membership functions for an application of the fuzzy microcontroller of the present invention.
Detailed Description of the Preferred Embodiment
The preferred embodiment of the present invention will now be described with reference to the accompanying drawings.
FIG. 3 discloses a block diagram of the fuzzy processor system according to a preferred embodiment of the present invention and designated generally by the reference character 50. The fuzzy processor system 50 may control a plurality of devices by outputting an output signal DOUT to a corresponding controlled device (not shown in FIG. 3). The value of the output signal DOUT is determined by the rules associated with the output signal and the signals input to the fuzzy processor system 50. In other words, each output signal is the result of a predetermined set of rules which define the action to be taken by the output signal under certain input conditions. The fuzzy processor system 50 of the present invention compares the input signals to each of the terms contained in the rules, one term and one rule at a time, to determine which rule will provide the optimum output.
As shown in FIG. 3, the fuzzy processor system 50 includes a set of selectors 60, 62 and 64, a fuzzy input processor 70, a fuzzy output processor 80, a fuzzy memory interface 90, a byte-wide memory storage device 100, and a set of output latches 110 and a selector circuit 120.
Although not shown, it is assumed there is an external counter for controlling the output latches 110. One of ordinary skill in the art would be able to readily implement the counter, and any other necessary overhead logic in view of the following description.
The input selector 60 selects one of a plurality of inputs as data to be fuzzified in response to a 5-bit input select signal from the fuzzy memory interface 90. The inputs may be from any external source such as a multiplexer (not shown) which time-multiplexes a plurality of sensor signals, for example. Although the input signal may be either analog or digital, the preferred embodiment uses an eight (8) bit digital word so that the input selector 60 receives a data stream of 8-bit digital input signals from each of the external sources. One of ordinary skill in the art will realize that the input selector 60 can accommodate analog TDM inputs by configuring the input selector 60 for TDM inputs and including an 8-bit A/D converter.
The center selector 62 and the width selector 64 select one of a plurality of inputs for center and width data in response to 4-bit center select and width select signals, respectively supplied by the fuzzy memory interface 90. The center select and width select signals, supplied by the fuzzy memory interface 90, enable a designer to create a variety of membership functions by combining the center and width parameter data from different sources, as contrasted to copending applications 07/712,871 and 07/893,093, which required specifically-defined membership functions and rules. Like the input selector 60, the inputs to the center selector 62 and the width selector 64 can be any digital inputs, such as a stored value, a digitized sensor signal, or a clock signal. Therefore, the center or width of any membership function can be either a fixed or a dynamic value based upon time, temperature, force, distance, etc. As a result, the present invention enables the processing of dynamic membership functions. This feature is particularly effective in control systems having instability problems, and enables a self-adapting fuzzy logic system by defining the center and width of membership function on the basis of dynamic parameters.
FIG. 3 shows that the fuzzy memory interface 90 also supplies center and width data to the center selector 62 and the width selector 64, respectively. Thus, the fuzzy memory interface 90 enables the fuzzy input processor 70 to receive the fuzzy set parameter data from a variety of sources, including the external memory storage device 100, which may be implemented on the same integrated circuit or on a separate circuit.
The fuzzy input processor 70 receives the selected 8-bit input data (DIN), center data (CEN) and 6-bit width data (WID) from the corresponding selector. The fuzzy input processor 70 also receives a 3-bit type command signal (TYPE) from the fuzzy memory interface 90 that indicates the type of membership function being used (described in detail below).
The selected input data signal (DIN) is fuzzified in accordance with the corresponding fuzzy set parameter data, namely center data (CEN), width data (WID) and the type command signal (TYPE).
The fuzzy input processor 70 sequentially processes the supplied data to determine the minimum rule term for each rule of a given output and the relative maximum for all the rules processed for the given output. In other words, a given output has a set of predetermined rules. These rules could, however, be shared across many outputs. Each rule has a set of rule terms that define the rules. As a result, the data inputs corresponding to a single rule are grouped together for successive processing.
The fuzzy input processor 70 successively processes the selected group of fuzzy inputs in accordance the rule being processed. The fuzzy input processor 70 determines whether the fuzzy logic rule has a likelihood of being executed on the basis of the fuzzified input signals. For example, a rule may be of the form "IF (Temp IS HOT) AND (Beta IS VERY.sub.13 HUMID) AND (Motor IS LOW) THEN INCREMENT Motor BY BIG.sub.-- INCREASE". Each of the terms (e.g., "Temp IS HOT") of the rule is compared with the corresponding fuzzy input (e.g., "Temp") to determine whether there is a low or high correlation. The correlations of each of the rule terms are then compared to determine the minimum level of correlation amongst the rule terms. The minimum rule term (e.g., the rule term having the minimum level of correlation) represents the overall likelihood of the rule being executed on the basis of the fuzzified input signals. The fuzzy input processor 70 determines the minimum rule term for each rule sequentially, for example, one rule at a time. As described in detail below with respect to FIGS. 8A and 8B the minimum rule term can be saved and applied to the subsequent rule being processed, thereby concatenating a plurality of rules to determine the minimum rule term.
The fuzzy input processor 70 outputs the minimum rule term as a 6-bit minimum term signal (MIN) and sets a strobe (MINCLK) that indicates new minimum data that is valid for data access. The fuzzy input processor 70 also compares the minimum rule term of the selected rule with the minimum rule terms of other corresponding rules and identifies the rule which has the maximum value for the minimum rule term. As described in detail below, the fuzzy input processor 70 outputs an action clock (ACT CLK1) when the minimum rule term under comparison (output as the current minimum term signal (MIN)) is greater than the previously stored minimum rule term, e.g., whenever a new winning rule has been determined. The fuzzy input processor 70 outputs a second action clock (ACT CLK2) at the end of the processing cycle of the last rule of a given output. As discussed in detail below, the last rule of a given input is identified by the type command signal (TYPE).
Therefore, the winning rule for a given output can be determined by monitoring the action clock (ACT CLK1), and can be used to latch an action address into an external register. The action clock (ACT CLK2) verifies the last occurrence of the action clock (ACT CLK1) as representing the optimum rule for the given output.
The fuzzy output processor 80 receives the 6-bit minimum rule term signal (MIN) and the second action clock (ACT CLK2) from the fuzzy input processor 70. The fuzzy output processor also receives an 8-bit action value of a processed rule (ACTION) and a 2-bit defuzzifying mode signal (MODE) from the fuzzy memory interface 90. FIG. 3 shows that the fuzzy output processor 80 also receives a selected feedback signal (DATA IN) from a selector 120. As described in detail below, the fuzzy output processor 80 outputs a result (DOUT) for the winning rule to output latches 110 in accordance with one of the following selectable modes: Immediate, Accumulate, and Weighted Average. The mode is selected in accordance with the defuzzifying mode signal (MODE).
The fuzzifying method of the present invention will now be disclosed with reference to FIGS. 4 and 5A-5F. FIG. 4 illustrates a relationship between a crisp input 38 and two arbitrarily selected and exemplary fuzzy sets 40 and 42 having membership functions A and B. The term "crisp" refers to any input or output which yields a single, precise value. Since most sensors produce, and most actuators require, scalar values, the fuzzy processor system 50 of the present invention accepts crisp inputs and can provide crisp outputs.
Each membership function specifies a relationship between the crisp input 38 and the corresponding fuzzy set. In other words, the membership functions determine the size of the fuzzy sets 10 shown in FIG. 1C. The evaluation of the conjunction of the crisp input 38 with a membership function results in a "fuzzy term": the term represents the degree of membership or non-membership of the crisp input 38 in the corresponding fuzzy set. Since it is a fuzzy number, the term may be used in subsequent fuzzy rules, similar to Boolean operations.
Thus, the membership function in a fuzzy system defines the degree of similarity between an unknown input and a known value or class (for example, the fuzzy set). As shown in FIG. 5A, the degree of membership .mu..sub.a for a crisp input A having a value X.sub.a occurs at the point where the crisp input A intersects the membership function C. However, due to nonlinearities in the control system (for example, sensor characteristics, control responses, dynamic responses) the membership function .mu. may be nonlinear relative to the input dimension X, making the determination of the membership function shape, and hence the similarity determination, difficult to implement.
In order to avoid the determination of the membership function shape, the fuzzifying method of the present invention predefines the slope of the membership function, and determines the distance between the crisp input and the center of the membership function. As shown in FIG. 5B, the membership function D is defined to be linear with a predetermined slope (for example, the slope is one-to-one). Rather than determining the intersection point of the membership function and the crisp input X.sub.A, the fuzzifying method measures the distance da by subtracting the crisp input from the known center value and ignoring the sign. The difference is then complemented with respect to the width of the membership function D so that if the input and the center are the same, then the similarity value da is a maximum, whereas if the input is further from the center, the similarity value approaches a minimum.
FIGS. 5C and 5D show a step membership function having true (inclusive) and inverse (exclusive) polarity, respectively. The dashed lines extending from the membership function illustrate the shape of the membership function if the width was set at a maximum. In other words, although the width of the membership functions in FIGS. 5C and 5D are set at a value of "13", the maximum width according to the preferred embodiment is "63". Therefore, if a crisp input X was outside the membership function of FIG. 5C, the similarity value would be forced to the minimum or zero.
FIGS. 5E and 5F show membership functions for Greater Than and Less Than membership functions, respectively, each having inclusive polarity. In order for a crisp input to be a member of the Greater Than membership function, the distance between the crisp input and the center value must be greater than or equal to the lower boundary LB of the preset width value; conversely, in order for a crisp input to be a member of the Less Than membership function shown in FIG. 5F, the distance between the crisp input and the center value must be less than or equal to the upper boundary UB of the preset width value. It will be recognized using conventional Boolean operations as applied to fuzzy logic that a Greater Than membership function having inverse (exclusive) polarity will be equal to a Less Than membership function having a true (inclusive) polarity.
The advantages of the fuzzifying method of the present invention are that a designer of a control system is not required to determine a membership function shape for all fuzzy sets, and also that the fuzzifying method can be easily implemented in hardware. Further, the Less Than and Greater Than membership functions are asymmetrical membership functions. Consequently, utilization of both asymmetrical membership functions, via rule selection, provides the ability to define an adjustable height membership function. A preferred embodiment of the fuzzifying method is disclosed with reference to FIGS. 7A and 7B (see below).
FIG. 6 shows a functional block diagram of the fuzzy input processor 70 of FIG. 3. The fuzzy input processor 70 includes a fuzzifier 700, a Min/Max circuit 705 and a control circuit 750. The Min/Max circuit 705 includes a minimum comparator 710, a minimum register 720, a maximum comparator 730 and a maximum register 740.
The fuzzifier 700 fuzzifies the 8-bit input data (DIN) in accordance with the supplied fuzzy set parameter data, namely the 8-bit center data (CEN), the 6-bit width data and the 3-bit type command signal (TYPE). The data is latched on the rising edge of the clock (MCLK). The type command signal (TYPE) identifies which of the membership functions of FIGS. 6C-6F are being processed. Alternately, the type command signal (TYPE) indicates the completion of processing of the rule terms for a given rule, or the completion of the last rule for a given output. Table 1 summarizes the 3-bit definitions for the type command signal (TYPE):
Thus, the fuzzifier 700 outputs the fuzzified input as a rule term (T) to the minimum comparator 710 in accordance with the type of membership function selected.
The control circuit 750 outputs the strobe signals ACT CLK1, ACT CLK2, ADCLR and MINCLK in accordance with the operations of the fuzzifier 700 and the Max/Min comparator 705. The address clear (ADCLR) signal is used to set any external rule memory address counter and is set following the action clock (ACT CLK2) or whenever the RESET signal is received.
FIG. 7A and 7B shows the preferred schematic diagram of the fuzzifier 700 of FIG. 6. The fuzzifier 700 determines the similarity of a crisp input to a selected fuzzy set using the fuzzifying method disclosed with reference to FIGS. 5C-5F. The 8-bit input data (DIN) and center data (CEN) are supplied to a subtractor circuit 702. The subtractor circuit 702 outputs a difference signal (D) representing the distance between the crisp input and the center value. The difference signal (D) from the subtractor 702 is input to a width comparator 704 and compared with the membership function width value (WID). The width comparator 704 compares the difference signal (D) with the width value (WID) and outputs a comparison signal (A>B) to a control logic circuit 706.
The control logic circuit 706 determines whether the fuzzified value should be forced to a maximum value or a minimum value in accordance with the type command signal (TYPE), the comparison signal (A>B), and an overflow bit from the subtractor circuit 702. Thus, the control logic circuit 706 receives an overflow bit from the subtractor 702 to determine whether the distance exceeds the maximum possible width of the membership function. According to the preferred embodiment, the maximum width is sixty-three (decimal); thus, the overflow bit output from the subtractor 702 indicates whether the distance from the center to the crisp input exceeds the maximum width.
The control logic circuit 706 outputs control signals to an alpha cut calculator 708 in response to the result of the comparison from the comparator 704, and the configuration signals identifying the membership mode. The alpha cut calculator 708 outputs the fuzzified data from the difference of the subtractor 702 using the following logic: if the crisp data is outside the membership function (e.g., the result of comparison indicates the difference value is greater than the width of the membership function), then the fuzzified value is forced to zero if the polarity bit is not set (e.g., membership function is inclusive or "TRUE"); however, if the polarity bit is set, then the fuzzified value is forced to zero if the crisp data is "inside" the membership function.
In accordance with the fuzzifying method of the present invention, since the membership function has a one-to-one linear slope, the alpha cut calculator 708 takes the complement of the difference value and outputs the result as a similarity value, representing the degree of membership to the fuzzy set. The complement is determined as follows. According to the exemplary membership function presented in FIG. 5C, the maximum width of the membership function is "63" decimal, so that if a crisp input had, for example, a value "CI=7", and a selected membership function had a center location "CL=13" decimal and a width "W=9", then the difference "DIFF=13-7=6" would be complemented to result in a similarity value "COMP(DIFF)=ABS(6-63)=57" decimal. Thus, the similarity value is "57", with the maximum value being "63" if the difference DIFF was zero. As a result, the fuzzifying method of the present invention provides a simple and effective method for similarity determination between a crisp input and a fuzzy set.
The alpha cut calculator 708 includes an invertor circuit 708a, an AND circuit 708b, and an OR circuit 708c, each of which receive the control signals from the control logic circuit 706. The invertor circuit 708a performs a 1's complement inversion of the lowest six bits of the difference signal output from the subtractor 702 in accordance with the inclusive/exclusive bit from the type command signal (TYPE). The AND circuit 708b clears all the bits of the difference value when a "0" is received from the control logic circuit 706, and the OR circuit 708c sets all the bits of the difference value when a "1" is received from the control logic circuit 706.
The alpha cut calculator 708 also sets the term value (T) in response to a type decoder 709. The type decoder 709 outputs a set signal (SET) to the OR gate 708c whenever the type command signal indicates a last term (LT), a last rule (LR), or whenever the system reset signal (RESET) is activated.
FIGS. 8A and 8B discloses the Min/Max comparator 705 and a portion of the control circuit 750 of FIG. 6. Although FIG. 6 functionally shows the minimum comparator 710 and the maximum comparator 730 as separate elements for illustration purposes, the two comparator functions are performed by the same Min/Max comparator circuit 712 in order to minimize silicon area on an IC. Similarly, one of ordinary skill in the art will realize that the type decoder 709 and the control circuit portion 752 in combination Rake up the control circuit 750.
FIGS. 8A and 8B shows that the fuzzified input term (T) from the fuzzifier 700 and the stored minimum value (MIN) from the minimum register 720 are supplied to the Min/Max comparator 712. The Min/Max comparator circuit 712 compares the fuzzified input term (T) and the stored minimum value (MIN) and outputs a result of comparison. Specifically, the NAND gate 714 outputs a result of comparison to the NOR gate 716 and the NAND gate 718. The NOR gate 716 and the NAND gate 718 output appropriate latch signals to the minimum register 720 and the maximum register 740, in accordance with the LT.LR control signal from the type command signal decoder 709. Thus, the NOR gate 716 outputs a minimum enable signal (MIN.sub.13 ENA) only when the type command signal does not indicate the last term or last rule, when the reset signal is not set, or when the NAND gate 714 indicates that the term value (T) is less than the stored minimum value (MIN) (active low).
Conversely, the NAND gate 718 outputs a maximum enable signal (MAX.sub.-- ENA) (active low) to the maximum register 740 when the NAND gate 714 outputs a signal indicating that the stored minimum value (MIN) is greater than the stored maximum value, and when the type command signal (TYPE) indicates the last rule is being processed.
The Min/Max comparator 712 includes a series of selectors 719 that select either the fuzzified input term (T) or the stored maximum value from the maximum register 740 in response to the LT.LR control signal from the type decoder 710. Thus, the type command signal (TYPE) controls whether the Min/Max comparator 712 operates in a minimum or maximum mode.
The control circuit portion 752 outputs the strobe MINCLK in response to the LT.LR control signal from the type decoder 710 shown in FIGS. 7A and 7B The control circuit portion 752 also outputs the clock signal (ACT CLK1) in response to the maximum enable signal (MAX.sub.-- ENA) from the NAND gate 718. As will be readily apparent to those of ordinary skill in the art, the strobe MINCLK becomes active on every last term or last rule providing that the result of all terms of that rule is greater than 0. The MINCLK signal is used by the fuzzy output processor in order to provide a weighted output average output (discussed in detail below).
The action clock (ACT CLK1) is generated on every last term or last rule provided that the minimum data is greater than or equal to the stored maximum data. Finally, the action clock (ACT CLK2) is generated on every last rule provided that the rule contained at least one action clock signal (ACT CLK1).
Thus, the action signal (ACT CLK1) can be used to identify the latest winning rule in order to restore the relevant action values related to that winning rule. Further, the action clock (ACT CLK2) can be used by external logic to latch the action data accessed during the occurrence of the action clock (ACT CLK1).
Finally, the address clear (ADCLR) is generated every last rule, and is utilized to clear the maximum register 740. The address clear (ADCLR) may also be used to clear external memories.
FIG. 9 is a timing diagram of the fuzzy input processor 70 shown in FIG. 6. FIG. 9 assumes that there is an external rule address counter for processing each rule of an output. As shown in FIG. 9, when the reset signal (RESET) is active (active low), the address clear (ADCLR) signal becomes active, clearing the rule address counter. The selected data is supplied to the fuzzy input processor 70 when the address clear (ADCLR) signal goes high. Data is clocked in during the rising edge of the master clock (MCLK). Exemplary data values are shown in parentheses as hexadecimal values. During each clock cycle, the fuzzified data is successively compared to the previous data to determine the minimum rule term, namely the crisp input having the farthest distance away from a membership function center. As shown in FIG. 9, the fourth data input (DATA IN) during processing of Rule 1 has a value of 62 hex. When compared with the center value of 78 hex, the hexadecimal difference is 16. When complementing the unsigned difference to six bits, the result is 29 hex.
The minimum value, 29 hex, is compared at the maximum comparator 730 and stored in the maximum register 740 if the value is larger than the previously stored value. Updating the maximum register causes an action clock (ACT CLK1) to be generated. The minimum clock (MINCLK) occurs when the type command signal (TYPE) has a value of 1, indicating the last rule term has been processed. This process continues for all the rules, until the last rule command is issued, in other words, when the type command signal (TYPE) has a value of 0. At that time, the fuzzy input processor 70 outputs the action clock (ACT CLK2) and the address clear (ADCLR), which starts a new cycle.
FIG. 10 discloses a block diagram of the fuzzy output processor 80 of FIG. 3. The fuzzy output processor 80 includes a centroid calculator 810, also referred to as a rule mode processor, which calculates a centroid value, also known as a weighted average. The fuzzy output processor 80 also includes an adder 820 to provide an accumulate mode. Specifically, the action value corresponding to a winning rule is added to the previous output, which is supplied to the fuzzy output processor 80 via the DATA IN signal line (see FIG. 3). The signal paths DATA IN and the ACTION signal lines are also directly supplied to a select circuit 830, which selects one of the output modes as the output action value (DOUT) in accordance with a mode signal from a control circuit 840. The mode signal, supplied from the fuzzy memory interface 90 shown in FIG. 3, identifies the output mode for the corresponding winning rule.
The centroid calculator 810 includes a multiplier 812 which receives the 6-bit value signal (MIN) from the fuzzy input processor 70. The multiplier 812 also receives the 8-bit action value and supplies the multiplied result to a summation circuit 814. The minimum value signal (MIN) is also supplied to a summation circuit 816. The summation circuits 814 and 816 output their respective summation results to a dividing circuit 818, which provides the weighted average value, or centroid value, to the select circuit 830.
The Weighted Average mode causes the fuzzy output processor 80 to provide an averaged value of all the actions associated with the winning rules from the Min/Max comparator to be applied to the output action value (DOUT). Thus, the weighted average mode modifies the output by the average of the actions of all rules where a membership was considered valid. Specifically, the weighted average mode implemented by the centroid calculation circuit 810 follows the equation: ##EQU1## where W is the average action value or centroid value output to the select circuit 830, n is the number of rules used by an output, m.sub.i is the minimum of terms value (MIN) for rule i, and a.sub.i is the action value (ACTION) assigned to the rule i. The rule mode processor 810 multiplies the minimum of terms value MIN with the corresponding action value ACTION and accumulates the weighted average W until the last rule for a corresponding output is processed, and divides by the accumulated minimum of terms value MIN.
Alternatively, normalization can be performed by dividing the accumulated value m.sub.i * a.sub.i by the number of rules n multiplied by the value "63", which is the height of the membership function. The normalization can also be performed by dividing the accumulated value by "64" by performing a bit shift of the numerator. One will recognize that the resulting error will be negligible.
The number of rules n used by an output and the identity of a last rule for each output is determined by the action clock (ACT CLK2), whereby the control circuit 840 outputs the mode signal (MODE) to the selector circuit 830.
The timing for the rule processing will now be described. FIG. 11 shows a timing diagram of the fuzzy output processor 80. The fuzzy output processor 80 processes a rule in response to the MINCLK strobe until the last rule used has been processed, indicated by the action clock (ACT CLK2) going active low. The control circuit 840 of the fuzzy output processor 80 outputs a BUSY signal to inform external circuitry that the fuzzy output processor 80 is busy processing a rule.
Although the outputs of the selector circuit 830 are digital, the output can be modified by adding a D/A converter to the selector circuit 830 or the latches 110 in FIG. 3, in order to provide an analog output signal. Further, the latches 110 in FIG. 3 can be arranged to provide TDM outputs.
FIG. 12A discloses a block diagram of the fuzzy memory interface 90 of FIG. 3. The fuzzy memory interface 90 receives an 8-bit memory data signal (MEMORY DATA), the master clock signal (MCLK), and the address clear signal (ADCLR) from the fuzzy input processor 70. The memory data signal (MEMORY DATA) represents the configuration data from an external byte wide memory for a rule to be processed. Thus, the memory data signal (MEMORY DATA) is a combination of term, rule and action data.
The byte-wide memory 100, shown in FIG. 3, is organized in three sections. These sections are defined as rule/term storage, center storage, and width storage. The memory organization is illustrated in Table 2.
As described previously, rules are organized as groups of one or more terms. FIG. 12B shows that each term is made up of two bytes. The first byte always is stored at an even address, and selects the input and type for the respective term. When the type is the last term, the input select field is used for selecting the defuzzification mode as well. The second byte, which is stored at an odd address, contains the center and width select fields. These fields point to the address having the desired center and width data. When the last type is last term, this byte then contains the action data value, or alternately could contain the address which points to the desired action data.
Therefore, it can be seen that the number of bytes required for any rule is
Bytes per rule=((2X)# of terms)+2.
The last 32 bytes of the addressed external memory are used to store the 16 widths and centers. When used, this data is output on the center and width data buses, and the 4-bit addresses for these parameters are output on the center and width select buses.
Thus, the appropriate address data is loaded into the fuzzy memory interface 90 via the memory data signal line (MEMORY DATA). Specifically, the latch 902 obtains the input select or mode data from the first byte of the rule memory portion of the parameter storage 100. The latch 902 also obtains the 3-bit type select data from the first byte and supplies it to a decoder 912. The latch 904 receives the center select and width select data from the second byte of the rule memory. The latches 906 and 908 receive center data and width data, respectively, addressed from the external memory. As shown in FIG. 3, the center data and width data are supplied to selectors 62 and 64, respectively. Similarly, the latch 910 receives the action data value from the second byte of the rule and outputs the action value to the fuzzy output processor 80.
Address generation for the external memory 100 is performed by the control and selection circuit 920, the 10-bit counter 922, and the 2:1 selector 924.
Operation of the fuzzy memory interface 90 will now be described with reference to FIGS. 12A and 13. The initialization is controlled by the address clear signal (ADCLR), whereby all transitions occur after the rising edge of the master clock (MCLK). The first clock causes the type command (TYPE) and input select data (INPUT SEL) to be read from the memory and latched by latch 902. The next clock latches the center and width select data to the latch 904. This address is used by the control selection circuit 920 to retrieve the actual center and width data during the next two clocks, which are thereafter stored in latches 906 and 908, respectively. The fourth clock cycle then repeats until a last term is decoded. This detection causes the action data to be latched in latch 910, and passed on to the fuzzy output processor 80. The first two addresses for the external memory originate from the 10-bit address counter 922 during the first two clocks. The address counter 922 is then disabled from counting for the next two clocks. The addresses during these clocks are created by the control and selection circuit 920 and the center select and width select information is stored in latch 904. When the address clear (ADCLR) signal is asserted, the control and selection circuit 920 resets the counters, and the entire cycle begins again from address O.
One of ordinary skill in the art would realize that if it is not necessary to loop inputs or outputs back as centers or widths, the selectors 62 and 64 shown in FIG. 3 could be eliminated. In this case, the 8-bit center and 6-bit width data from the fuzzy memory interface 90 would be connected to the fuzzy input processor.
One of ordinary skill in the art would realize that the fuzzy memory interface 90 provides flexibility in the memory configuration of rule terms. However, the fuzzy memory interface could be replaced by other logic controlling the addressing of rule terms to the fuzzy input processor 70 and the fuzzy output processor 80.
The fuzzy microcontroller of the present invention provides efficient control using fuzzy logic, with high processing rates using minimum silicon space. The fuzzy microcontroller also provides an efficient method for compressing input and output data by time division multiplexing. Further, the fuzzy microcontroller provides a fuzzifying arrangement (apparatus and method) which is easy to implement by avoiding the necessity of determining a shape of a membership function. The availability of a floating fuzzifier, whereby a center is defined on the basis of a second input, enables more complex membership functions to be easily implemented. Further, the use of an adjustable width membership function enables a simple implementation of an adaptive fuzzy logic system.
The operation of the fuzzy microcontroller of the present invention will become more apparent with reference to FIGS. 14A, 14B and 14C, which illustrate an application of the fuzzy microcontroller in controlling a vacuum cleaner. Specifically, FIGS. 11A, 11B and 11C each show an input to the fuzzy microcontroller along the X axis (e.g., pressure, dirt and texture), along with a plurality of fuzzifiers for classifying the input data.
As shown in FIG. 14A, the vacuum pressure input may be in one or more of five fuzzy sets: very low, low, medium, high or very high. Similarly, the inputs for dirt and texture (as measured by corresponding sensors) have their respective fuzzy sets as shown in FIGS. 14B and 14C, respectively.
Assuming the vacuum cleaner has three outputs to be controlled, namely VACUUM CONTROL, BEATER HEIGHT and BEATER SPEED, each output will have a number of associated rules to be applied, depending on the inputs pressure (P), dirt (D) and texture (T). Exemplary rules for the VACUUM CONTROL, BEATER HEIGHT and the BEATER SPEED are shown in Tables 3, 4 and 5, respectively.
The fuzzy microcontroller inputs the crisp inputs for pressure dirt and texture, and thereafter fuzzifies the data in accordance with the membership functions shown in FIGS. 14A-14C. The fuzzy microcontroller then determines the minimum rule term for each rule, and then determines the maximum minimum rule for each output. For example, assuming the input values for pressure, dirt and texture were "8", "40" and "5", respectively, the minimum comparator and maximum comparator would provide the following results as shown in Table 6 for the VACUUM CONTROL output.
As shown in Table 6 with respect to Rule 1, the inputs dirt, texture and pressure are not within the respective membership function widths for HIGH, ROUGH and LOW, respectively; as a result, the corresponding membership value .mu. (designated M in Table 8) for these fuzzy sets is forced to zero. However, the inputs do fall within the membership function widths for MLOW, SMOOTH and HIGH as shown for Rule 3 in Table 8. Since the minimum rule term for Rule 3 is "26", and since it is the relative maximum of minimum, the Rule 3 is selected as the optimal output for the output.
Of course, the membership function definitions may be modified to provide a floating center and a variable width to provide a controller able to adapt to varying conditions.
According to the present invention, the method of fuzzifying a crisp input may be easily implemented in an IC chip by determining the distance between the crisp input and the center of the respective membership function; if the distance is less than the width of the membership function, then the distance value is complemented to determine the alpha cut, or the membership value of the crisp input with respect to that fuzzy set. Since the shape of the membership function for the fuzzy set need not be determined, the fuzzifying method of the present invention can be easily implemented, which results in substantial cost savings in design development and circuit implementation.
In addition, the fuzzy input processors of the present invention can be implemented in a cascaded arrangement, in order to provide greater processing capacity for a larger number of inputs, outputs, or both. Those skilled in the art will realize the technique of controlling a plurality of the fuzzy input processors in such a cascaded arrangement.
Those skilled in the art of fuzzy logic will also appreciate that the programmable rule base for an output can include, but not be limited to have any number of rules, any number of terms in a rule, the use of any or all logical functions as sentence connective operators, a permanent or volatile rule memory with on-chip or external rule storage.
Further, the rule base can have various configurations and include address pointers rather than data values to optimize rule memory storage capacity and flexibility.
Finally, those skilled in the art of fuzzy logic will appreciate that techniques for the determination of an optimum output, also known as "defuzzification", include but are not limited to defuzzification by the Mean of Maximum method, the Maximum method, the Center of Area or Center of Gravity methods, or equivalent methods thereof.
While this invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiment, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims
What is claimed is:
1. An apparatus for determining an optimum rule from a set of fuzzy logic rules corresponding to a given output, said fuzzy logic rules having rule terms corresponding to a set of membership functions, the apparatus comprising: means for fuzzifying at least one crisp input signal to a plurality of fuzzified signals in accordance with a corresponding plurality of said membership functions, each of said membership functions including a predetermined linear slope and predetermined fuzzy set parameter data including width, center and membership function type, said fuzzifying means comprising: means for determining a distance of said at least one crisp input signal from said center of each of said membership functions, and means for linearly complementing each of said distances relative to said corresponding width of said membership functions, said complemented distances being output as said fuzzified signals, respectively; means for receiving said at least one fuzzified input signal and for determining a minimum rule term for each of said rules in response to said at least one fuzzified input signal, said determining means successively comparing said minimum rule terms for each of said rules corresponding to said given output to determine a maximum among said minimum rule terms; and means for receiving said minimum rule terms and for identifying said maximum among said minimum rule terms by outputting a maximum rule signal when said maximum among said minimum rule terms has been determined, said maximum among said minimum rule terms corresponding to said optimum rule.
2. An apparatus as claimed in claim 1, further comprising: means for executing said optimum rule in accordance with rule data including an action value and an action mode corresponding to said optimum rule, said executing means outputting an output signal in response to said maximum rule signal, said executing means selecting as said output signal, in response to said action mode, one from a group of an immediate output, an accumulate output, and a weighted average output.
3. An apparatus as claimed in claim 2, further comprising: means for supplying said membership function parameter data to said fuzzifying means and said rule data to said executing means, said supplying means accessing said membership function data from an external memory in accordance with said set of fuzzy logic rules corresponding to said given output, said means for supplying accessing and supplying membership function parameter data and rule data for a second set of fuzzy logic rules corresponding to a second output in response to said maximum rule signal from said identifying means.
4. An apparatus as recited in claim 1, wherein at least one of said centers of said membership functions represents a dynamic input that causes said at least one membership function to operate as a floating center.
5. An apparatus as recited in claim 4, further comprising means for supplying to said fuzzifying means a selected input as said floating center.
6. An apparatus as recited in claim 4, wherein at least one of said widths of said membership functions represents a dynamic input that causes said at least one membership function to operate as a dynamic width membership function.
7. An apparatus as recited in claim 6, further comprising means for supplying to said fuzzifying means a selected input as said dynamic width membership function.
8. A method for fuzzifying a crisp input signal, comprising the steps of: defining fuzzy set parameters including a center and a width, said center indicating a maximum membership value and said dynamic width indicating minimum membership values for said fuzzy set; applying a plurality of signals and said fuzzy set parameters to a selector circuit; selecting one of said plurality of signals as a center signal in accordance with said center; inputting said crisp input signal and said center signal to a subtractor; outputting from said subtractor a distance signal representing a distance of said crisp input signal from said center; applying a plurality of second signals to a second selector circuit; selecting one of said plurality of second signals as a width signal in accordance with said width; comparing said distance signal with said width signal and outputting a result; outputting a similarity signal in response to said comparing result, said outputting step linearly complementing said result with respect to said width of said fuzzy set in accordance with a polarity fuzzy set parameter so that: said similarity signal is a maximum if said distance signal represents a minimum distance of said crisp input signal from said center, and said similarity signal is a minimum if said distance signal represents a maximum distance of said crisp input signal from said center.
9. A method as recited in claim 8, further comprising the steps of: storing a plurality of rules having rule terms in a first portion of an external memory, said rule terms corresponding to a plurality of said fuzzy sets including said fuzzy set having said dynamic center and dynamic width, said plurality of rules corresponding to a given output; storing said plurality of fuzzy sets in a second portion of said external memory; sequentially providing said rule terms from said external memory for each of said rules to a memory controller circuit in accordance with a memory address signal from said memory controller circuit, said memory controller circuit accessing said center and width of one of said fuzzy sets stored in said memory in accordance with said provided rule term, said memory controller circuit providing said center and width of said one accessed fuzzy set to said first and second selector circuits, respectively, said similarity signal outputting step successively outputting similarity signals for each of said sequentially provided rule terms.
10. A method as recited in claim 9, further comprising the steps of: storing input select values for each of said rule terms in said external memory, said memory controller circuit accessing one of said input select value in accordance with said corresponding provided rule term; applying a plurality of input signals to a third selector circuit; selecting one of said plurality of input signals as said crisp input signal by supplying said input select value from said memory controller circuit to said third selector circuit; and supplying said crisp input signal from said third selector circuit to said subtractor.
11. A method as recited in claim 9, further comprising the steps of: storing action values for each of said rules in said external memory; comparing said similarity signals for each of said sequentially provided rule terms to determine a minimum similarity signal for each of said rules; repeating said similarity signals comparing step for all said plurality of stored rules corresponding to said given output to determine a plurality of said minimum similarity signals for said rules, respectively; determining a maximum of said minimum similarity signals; identifying an optimum rule from said plurality of rules, said maximum of said minimum similarity signals corresponding to one of said rule terms of said optimum rule; and executing said optimum rule by causing said memory controller circuit to access said action value stored in said external memory corresponding to said optimum rule.
12. A fuzzy logic processor system comprising: a fuzzy input processor for identifying an optimum rule from a plurality of rules corresponding to a given output in response to at least one crisp input signal, each of said rules having rule terms corresponding to a set of membership functions, each of said membership functions having a center, a width, and a membership function type, said fuzzy input processor successively processing each of said rules to determine said optimum rule, said fuzzy input processor comprising: a fuzzifier for fuzzifying said at least one crisp input signal in response to one of said membership functions, said fuzzifier including a subtractor for outputting a distance signal in response to said at least one crisp input signal and said center of said one membership function, a width comparator for outputting a width comparison signal in response to said width of said one membership function and said distance signal, and an alpha cut calculator for outputting a fuzzified term signal in response to said width comparison signal and said membership function type of said one membership function, a minimum comparator for outputting a minimum rule term for each of said rules, said minimum comparator outputting said minimum rule term in response to said fuzzified term signals for each of said rule terms of each of said rules, and a maximum comparator for identifying a maximum of said minimum rule terms, said maximum comparator successively comparing each of said minimum rule terms for all of said rules of said given output; and means for supplying said crisp input signal and said center, width and membership function type of each of said membership functions to said fuzzy input processor in accordance with said each rule being processed by said fuzzy input processor.
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