DocumentCode
1601521
Title
Adapted gradient algorithm for algebraic fuzzy neural networks
Author
Teodorescu, H.N. ; Arotaritei, D. ; Gonzalez, E.L. ; Mendana, A.
Author_Institution
Tech. Univ. Iasi, Romania
fYear
1996
Firstpage
179
Lastpage
186
Abstract
A learning algorithm based on a gradient technique is introduced for the algebraic fuzzy neural network with fuzzy weights. The fuzzy weights can be triangular fuzzy numbers (usually nonsymmetric), or trapezoidal fuzzy numbers. The network is able to map a vector of triangular (trapezoidal) fuzzy numbers into any other vector of triangular (trapezoidal) fuzzy numbers
Keywords
fuzzy neural nets; learning (artificial intelligence); vectors; adapted gradient algorithm; algebraic fuzzy neural networks; fuzzy weights; learning algorithm; nonsymmetric triangular fuzzy numbers; trapezoidal fuzzy numbers; vector; Arithmetic; Costs; Electronic mail; Fuzzy neural networks; Fuzzy sets; Level set; Multi-layer neural network; Neural networks; Neurons; Shape;
fLanguage
English
Publisher
ieee
Conference_Titel
Neuro-Fuzzy Systems, 1996. AT'96., International Symposium on
Conference_Location
Lausanne
Print_ISBN
0-7803-3367-5
Type
conf
DOI
10.1109/ISNFS.1996.603837
Filename
603837
Link To Document