DocumentCode
313626
Title
A training technique for fuzzy number neural networks
Author
Dunyak, James ; Wunsch, Donald
Author_Institution
Dept. of Math., Texas Tech. Univ., Lubbock, TX, USA
Volume
1
fYear
1997
fDate
9-12 Jun 1997
Firstpage
533
Abstract
A new technique is discussed for the training of fuzzy neural networks with general fuzzy number inputs, weights, and outputs. Fuzzy number neural networks are difficult to train because of the many alpha-cut constraints implied by the fuzzy weights. In this paper, an unconstrained representation is used for the fuzzy weights, allowing application of a standard backpropagation approach. The technique is demonstrated on a moderately large problem
Keywords
fuzzy neural nets; learning (artificial intelligence); alpha-cut constraints; fuzzy number neural networks; standard backpropagation approach; training technique; unconstrained representation; Constraint optimization; Constraint theory; Equations; Fuzzy neural networks; Fuzzy set theory; Fuzzy sets; Integrated circuit noise; Mathematics; Neural networks; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks,1997., International Conference on
Conference_Location
Houston, TX
Print_ISBN
0-7803-4122-8
Type
conf
DOI
10.1109/ICNN.1997.611725
Filename
611725
Link To Document