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Redundant fuzzy rules exclusion by genetic algorithms

✍ Scribed by A. Lekova; L. Mikhailov; D. Boyadjiev; A. Nabout

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
562 KB
Volume
100
Category
Article
ISSN
0165-0114

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✦ Synopsis

A genetic-algorithm-based method for exclusion of the potential redundant if-then fuzzy rules that have been extracted from numerical input-output data is proposed. The main idea is the input-space separation into activation rectangles, corresponding to certain output intervals. The generation of fuzzy rules and the membership functions are based on these activation rectangles and appropriate fuzzy rules inference mechanism is proposed. As the method usually produces too many rules, it is necessary to exclude the potential redundant if-then rules. The concept for varying the family of sensitivity parameters, defining the overlapping of the fuzzy regions is proposed. The genetic algorithms are used to resolve the following combinatorial optimization problem: the generation of families of sensitivity parameters. In this way the potential redundant if-then fuzzy rules are excluded.

The method formalizes the synthesis of the fuzzy system and could be used for function approximation, classification and control purposes. An illustrative example for implementation of the method for traffic fuzzy control is given, i~" 1998 Elsevier Science B.V. All rights reserved

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