[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5391/JKIIS.2002.12.3.261

MEMBERSHIP FUNCTION TUNING OF FUZZY NEURAL NETWORKS BY IMMUNE ALGORITHM

Kim, Dong-Hwa (Dept. of Instrumentation and Control Eng., Hanbat National University, 16-1 San Duckmyong-dong Yusong-Gu, Deajon City Seoul,)

Publication Information

Journal of the Korean Institute of Intelligent Systems / v.12, no.3, 2002 , pp. 261-268 More about this Journal

Abstract

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

Keywords

Immune networks; Fuzzy neural network; Auto-tuning; Membership function.;

Citations & Related Records

Reference

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