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Far East Journal of Applied Mathematics

Far East Journal of Applied Mathematics
Volume 22, Issue 2, Pages 191 - 200 (February 2006)

FUZZY NEURAL SYSTEM MODELING BASED ON THE EXTENDED FUZZY ENTROPY
In Kue Park (South Korea)

Abstract:
A fuzzy system should be identified through iteratively trial and error at initial design, even though it is efficient on the system managing imprecise and uncertain situations. To solve the problem like this, two self-generating fuzzy systems have been usually suggested. One is to fuse Fuzzy Neural Networks (FNNs) or to fuse Fuzzy Genetic Algorithms (FGAs). The other is to use pure gradient descent based method. The former has a drawback that we must understand the basic concept of other information processing methods like neural networks or genetic algorithms. The latter has also a drawback that the processing time slows as the number of fuzzy rules increases exponentially whenever a fuzzy membership function is generated.

In this paper, we suggest and implement a new self-generating fuzzy system without the use of other information processing techniques. This system can suppress exponentially increasing fuzzy rules and minimize the number of membership functions that precious self-generating fuzzy system algorithms possess.

Keywords and phrases: measure of fuzziness, Shannon function, fuzzy rule, genetic algorithm.

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P-ISSN: 0972-0960

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