Data processing: artificial intelligence – Fuzzy logic hardware – Having function generator
Reexamination Certificate
1998-09-04
2001-08-07
Davis, George B. (Department: 2122)
Data processing: artificial intelligence
Fuzzy logic hardware
Having function generator
C706S902000
Reexamination Certificate
active
06272477
ABSTRACT:
TECHNICAL FIELD OF THE INVENTION
This invention relates generally to the field of computer modeling and more specifically to a hyper trapezoidal fuzzy dynamic state interpreter.
BACKGROUND OF THE INVENTION
The modeling of dynamic systems is an important step in the development of computer based interpretation of dynamic systems. Attempts have been made to model such diverse systems as financial markets, chemical plant operations and aircraft operations. However, typical statistical and numerical interpretation methods have failed because the parameters used to model systems often lead to faulty interpretation of the state that the system is in. This is because there are not always precise boundaries between system parameters and system states.
To help alleviate this problem, the concept of fuzzy sets was developed. The purpose of fuzzy sets is to deal with classes that have no sharply defined criteria of class membership. This helps to deal with the uncertainty that exists in complex dynamic systems.
Fuzzy systems are useful to help encode human knowledge into dynamic systems by allowing for the presence of uncertainty. Fuzzy systems consist of three parts, a fuzzifier, a rule and a defuzzifier. Inputs in the form of numerical data measured in the dynamic system are inputted into the fuzzifier. The fuzzifier prepares the exact numerical data for analysis in the rule base. The numerical data is mapped into fuzzy sets within a certain degree of certainty.
The fuzzy rule base encodes expert knowledge into sets of if/then rules. The number of rules depends on the complexity of the system, which is based on the number of inputs and the number of sets defined on each input domain. The defuzzifier converts the fuzzy output of the fuzzy rule base into exact numerical values if needed.
The drawback to this approach is that as the number of inputs increase, the size of the rule base grows exponentially. Thus, current fuzzy systems are inappropriate for complex dynamic systems. What is needed is an efficient way to apply fuzzy systems to interpret complex dynamic systems.
SUMMARY OF THE INVENTION
Accordingly, it may be appreciated that a need has arisen for a hyper trapezoidal fuzzy dynamic state interpreter in accordance with the teaching of present invention.
In one embodiment, an apparatus for determining the state of a dynamic system is disclosed. The apparatus comprises one or more sensors operable to sample physical parameters of a system, and a mode interpreter. The mode interpreter is operable to receive data from the sensor and determine the state of the dynamic system using hypertrapezoidal membership functions.
In another embodiment, a method for determining the state of a dynamic system is disclosed. In a first step a family of hypertrapezoidal membership functions are formulated which relate system parameters to system modes. Data from sensors monitoring the dynamic system's operation is received in a second step. In a third step, the state of the system is determined by applying the hypertrapezoidal membership functions to received data.
The present invention provides various technical advantages over current methods for interpreting dynamic systems. For example, one technical advantage is that complex dynamic system can be evaluated in an efficient manner without an extremely large rule base Other technical advantages may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.
REFERENCES:
patent: 5036730 (1991-08-01), Sakai et al.
patent: 5179625 (1993-01-01), Hisano
patent: 5491775 (1996-02-01), Madau et al.
patent: 5832187 (1998-11-01), Pedersen et al.
patent: 5953228 (1999-09-01), Kurtzberg et al.
patent: 5983161 (1999-11-01), Lemelson et al.
Kelly, III et al, “Soft Computing in the General Aviation Cockpit” Proceedings of the 1st On-Line Workshop of Soft Computing, Aug. 1996.*
Baranyi et al, “A General Interpolation Technique in Fuzzy Rule Bases with Arbitrary Membership Functions”, IEEE International Conference on Systems, Man and Cybernetics, Oct. 1996.*
Foster et al, “Optimal Set Placement and Multi Dimensional Fuzzy Sets for Fuzzy Controllers”, IEE International Conference on Control, 1994.*
Wallace E. Kelly, III, “Dimensionality in Fuzzy Systems”, A Dissertation, Aug. 1997, pp. 1-106.
John H. Painter, et al., “Decision Support for the Gneeral Aviation Pilot”, Proceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics, Orlando, FL, Oct. 12-15, 1997, pp. 1-6.
Wallace E. Kelly, III, et al., “Hypertrapezoidal Fuzzy Membership Functions for Decision Aiding”, WSC2: 2nd On-Line World Conference on Soft Computing, Jun. 23-27, 1997, pp. 1-9.
Wallace E. Kelly, III, et al., “Hypertrapezoidal Fuzzy Membership Functions,” Fifth IEEE International Conference on Fuzzy Systems, Sep. 8, 1996, pp. 1-6.
Kelly, III Wallace E.
Painter John H.
Baker & Botts L.L.P.
Davis George B.
The Texas A&M Univerisity System
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