DocumentCode :
2272441
Title :
Self-tuning fuzzy inference based on spline function
Author :
Shimojima, Koji ; Fukuda, Toshio ; Arai, Fumihito
Author_Institution :
Dept. of Mechano-Inf. & Syst., Nagoya Univ., Japan
fYear :
1994
fDate :
26-29 Jun 1994
Firstpage :
690
Abstract :
Recently, fuzzy systems are used in many fields and places. In order to apply the fuzzy system to wider fields, it is necessary to study the tuning methods of the fuzzy system. Some self-tuning methods were proposed so far. However these conventional self-tuning methods do not have sufficient capability of generalization. In this paper, we propose new self-tuning fuzzy neural networks. The fuzzy neural networks consist of membership functions that are expressed by spline function. Delta rule is applied to tune the membership functions and consequent parts. The effectiveness of the proposed methods is shown by some numerical examples
Keywords :
fuzzy neural nets; fuzzy set theory; generalisation (artificial intelligence); inference mechanisms; self-adjusting systems; splines (mathematics); Delta rule; fuzzy neural networks; fuzzy systems; generalization; membership functions; self-tuning fuzzy inference; spline function; Biomedical engineering; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy reasoning; Fuzzy systems; Input variables; Neural networks; Shape control; Spline;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1896-X
Type :
conf
DOI :
10.1109/FUZZY.1994.343652
Filename :
343652
Link To Document :
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