DocumentCode
325241
Title
The study of fuzzy functions. I. Universal approximators
Author
Buckley, James J. ; Feuring, Thomas
Author_Institution
Alabama Univ., Birmingham, AL, USA
Volume
1
fYear
1998
fDate
4-9 May 1998
Firstpage
750
Abstract
We show how to construct a large class of universal approximators for fuzzy functions (which continuously map fuzzy numbers into fuzzy numbers and are the extension principle extensions of continuous real-valued functions). One important application is that layered, feedforward, neural nets, with real weights and bias terms and fuzzy signals, whose output is computed using the extension principle, are universal approximators for these functions
Keywords
approximation theory; feedforward neural nets; fuzzy neural nets; multilayer perceptrons; bias terms; continuous real-valued function extensions; fuzzy functions; fuzzy number mapping; fuzzy signals; layered feedforward neural nets; multilayer feedforward neural nets; real weights; universal approximators; Differential equations; Feedforward neural networks; Fuzzy neural networks; Fuzzy sets; Neural networks; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on
Conference_Location
Anchorage, AK
ISSN
1098-7584
Print_ISBN
0-7803-4863-X
Type
conf
DOI
10.1109/FUZZY.1998.687582
Filename
687582
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