Fuzzy genetic learning automata classifier

Details Fuzzy genetic learning automata classifier Fuzzy genetic learning automata classifier

2005-01-04

2005-01-04

Starks, Jr., Wilbert L. (Department: 2121)

Data processing: artificial intelligence

Machine learning

Genetic algorithm and genetic programming system

C706S002000

Reexamination Certificate

active

06839698

ABSTRACT:
A method is provided for deriving a near-optimal fuzzy automaton for a given separation problem. The method includes the steps of: forming a first generation population (24) of fuzzy automata, where the first generation population of fuzzy automata includes a plurality of fuzzy automata; performing a mutation operation (28) on each fuzzy automaton in the first generation population of fuzzy automata; reproducing the first generation population of fuzzy automata using a survival of the fittest operation (30, 32, 34); and applying a cross-over operator (36) to the reproduced first generation population of fuzzy automata, thereby yielding a next-generation population of fuzzy automata. A near-optimal fuzzy automaton is identified by evaluating the performance (38) of each fuzzy automaton in the next-generation population; otherwise the methodology is repeated until a near-optimal fuzzy automaton is derived for the given separation problem.

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patent: 6400853 (2002-06-01), Shiiyama
Wee, William Go, “On Generalizations of Adaptive Algorithms and Application of the Fuzzy Sets Concept to Pattern Classification,” Thesis submitted to Purdue University, Aug. 1967.

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