DocumentCode :
3254397
Title :
Classification of fuzzy input patterns by neural networks
Author :
Ishibuchi, Hisao ; Morioka, Kouichi
Author_Institution :
Coll. of Eng., Osaka Prefectural Univ., Sakai, Japan
Volume :
6
fYear :
1995
fDate :
Nov/Dec 1995
Firstpage :
3118
Abstract :
In this paper we propose an approach to the classification of fuzzy input patterns by a multilayer feedforward neural network. Our neural network can handle linguistic inputs such us “small”, “medium” and “large” as well as fuzzy numbers such as “about 2” and “approximately 3”. First we briefly describe the input-output relation of our neural network for fuzzy input patterns. A fuzzy input pattern is mapped to fuzzy number outputs. Next we propose a classification method of the fuzzy input pattern. In the proposed method the grade that the fuzzy input pattern belongs to each class is calculated in the framework of possibility theory. Because our approach can handle linguistic values as inputs, it can also be utilized as a fuzzy rule generation method from the trained neural network
Keywords :
feedforward neural nets; fuzzy neural nets; fuzzy set theory; multilayer perceptrons; pattern classification; possibility theory; fuzzy input pattern classification; fuzzy numbers; fuzzy rule generation method; input-output relation; linguistic inputs; multilayer feedforward neural network; possibility theory; Arithmetic; Educational institutions; Electronic mail; Feedforward neural networks; Fuzzy neural networks; Fuzzy sets; Industrial engineering; Multi-layer neural network; Neural networks; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1995. Proceedings., IEEE International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-2768-3
Type :
conf
DOI :
10.1109/ICNN.1995.487282
Filename :
487282
Link To Document :
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