DocumentCode :
285097
Title :
A neural network model based on fuzzy classification concept
Author :
Kao, Cheng-I ; Kuo, Yau-Hwang
Author_Institution :
Inst. of Inf. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
Volume :
2
fYear :
1992
fDate :
7-11 Jun 1992
Firstpage :
727
Abstract :
A fuzzy-based neural network (FBNN) model, which applies a one-pass algorithm is proposed. The theory of the FBNN model originates from embedding a fuzzy classification concept into a parallel neural network architecture. Conventional neural networks, such as propagation using energy functions as learning principles, suffer from two major drawbacks, that of the local minimum problem and long training time. FBNN has the advantage of fast training, and avoids the local minimum problem. Experiments and comparisons between FBNN and some other neural network models are given. According to these results, FBNN shows stronger reliability on classification with respect to a probabilistic neural network, backpropagation, and a linear matching method
Keywords :
fuzzy set theory; neural nets; parallel architectures; pattern recognition; backpropagation; classification reliability; fuzzy classification; fuzzy set theory; linear matching method; neural network model; one-pass algorithm; parallel neural network architecture; pattern recognition; probabilistic neural network; Abstracts; Backpropagation algorithms; Data structures; Fuzzy neural networks; Fuzzy set theory; Fuzzy sets; Neural networks; Pattern classification; Pattern recognition; Polynomials;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
Type :
conf
DOI :
10.1109/IJCNN.1992.226901
Filename :
226901
Link To Document :
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