Title :
Neural nets can be universal approximators for fuzzy functions
Author :
Buckley, J.J. ; Hayashi, Yoichi
Author_Institution :
Dept. of Math., Alabama Univ., Birmingham, AL, USA
Abstract :
We first argue that the extension principle is too computationlly 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 :
arithmetic; feedforward neural nets; function approximation; fuzzy set theory; multilayer perceptrons; α-cuts; fuzzy functions; interval arithmetic; nonnegative fuzzy numbers; nonpositive fuzzy numbers; universal approximator; Arithmetic; Computer science; Feedforward neural networks; Fuzzy neural networks; Fuzzy sets; Mathematics; Neural networks;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.614430
