Title :
Neural net approximations to solutions of systems of fuzzy linear equations
Author :
Buckley, J.J. ; Hayashi, Yoichi
Author_Institution :
Dept. of Math., Alabama Univ., Birmingham, AL, USA
Abstract :
This paper continues previous research (Buckley and Eslami, 1995, Buckley and Hayashi, 1995, Hayashi and Buckley,1996) into using neural nets to solve fuzzy problems. We show how to train neural nets, with certain sign constraints on their weights, using genetic algorithms, to approximate solutions to systems of fuzzy linear equations. This paper presents a new application of layered, feedforward, neural nets with sign restrictions on their weights
Keywords :
feedforward neural nets; fuzzy set theory; genetic algorithms; multilayer perceptrons; fuzzy linear equations; fuzzy problems; genetic algorithms; layered feedforward neural nets; neural net approximations; sign constraints; sign restrictions; Arithmetic; Computer science; Equations; Feedforward neural networks; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Genetic algorithms; 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.1997.614433
