DocumentCode
328327
Title
Fuzzy neural networks based on spline functions
Author
Shimojima, Koji ; Fukuda, Toshio ; Arai, Fumihito
Author_Institution
Dept. of Mechano-Inf. & Syst., Nagoya Univ., Japan
Volume
1
fYear
1993
fDate
25-29 Oct. 1993
Firstpage
754
Abstract
Recently, fuzzy systems are used in many fields and places. In order to apply the fuzzy systems to wider fields, it is necessary to study the tuning methods of the fuzzy system. Some self-tuning methods have been proposed so far. However these conventional self-tuning methods do not have sufficient capability of generalization. In this paper, we propose a new self-tuning fuzzy neural network. The fuzzy neural network consists of membership functions that are expressed by spline functions. The 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; generalisation (artificial intelligence); learning (artificial intelligence); self-adjusting systems; splines (mathematics); delta rule; fuzzy neural networks; generalization; learning algorithm; membership functions; self-tuning; spline function; Biomedical engineering; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy reasoning; Fuzzy systems; Input variables; Shape; Spline; Tuning;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN
0-7803-1421-2
Type
conf
DOI
10.1109/IJCNN.1993.714023
Filename
714023
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