DocumentCode
315946
Title
Can neural nets be universal approximators for fuzzy functions?
Author
Buckley, J.J. ; Hayashi, Yoichi
Author_Institution
Dept. of Math., Alabama Univ., Birmingham, AL, USA
Volume
2
fYear
1997
fDate
1-5 Jul 1997
Firstpage
1101
Abstract
We first argue that the extension principle is too computationally involved to be an efficient way for a computer to evaluate fuzzy functions. We then suggest using α-cuts and interval arithmetic to compute the values of fuzzy functions. Using this method of computing fuzzy functions, we then show that neural nets are universal approximators for (computable) fuzzy functions, when we only input non-negative, or non-positive, fuzzy numbers
Keywords
feedforward neural nets; function approximation; fuzzy set theory; multilayer perceptrons; α-cuts; extension principle; fuzzy functions; interval arithmetic; neural nets; nonnegative fuzzy numbers; nonpositive fuzzy numbers; universal approximators; Arithmetic; Computer science; Feedforward neural networks; Fuzzy neural networks; Fuzzy sets; Mathematics; Neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1997., Proceedings of the Sixth IEEE International Conference on
Conference_Location
Barcelona
Print_ISBN
0-7803-3796-4
Type
conf
DOI
10.1109/FUZZY.1997.622863
Filename
622863
Link To Document