Paper:
Views over last 60 days: 425
Complexity Minimalization of Nonsingleton-based Fuzzy-Neural Network
Kin-fong Lei*, Péter Baranyi** and Yeung Yam*
*Department of Automation and Computer-Aided Engineering, The Chinese University of Hong Kong Shatin, Hong Kong
**Mechanical Research Group of Academy of Hungarian Science Department of Telecommunications and Telematics, Technical University of Budapest, H-1111, Budapest, Sztoczek u. 2, Hungary
Received:May 12, 2000Accepted:July 20, 2000Published:July 20, 2000
Keywords:SVD, GNN
Abstract
Singular value based reduction has been proposed for a singleton-based generalized neural network that is general in the sense that singleton-consequent-based fuzzy logic function generators are applied to define nonlinear weighting functions on connections among neurons. The product-sum-gravity inference technique with singleton consequent defines piece-wise linear approximation of nonlinear weighting functions. This paper proposed the use of nonsingleton-consequent-based product-sumgravity fuzzy algorithm that results in a piece-wise nonlinear approximation of weighting functions that considerably improve the approximation properties of the generalized network. This network is called a nonsingleton-based generalized neural network. The main objective of this technical report is to introduce the extension of the singular value based reduction technique to the nonsingleton-based neural network.
Cite this article as:K. Lei, P. Baranyi, and Y. Yam, “Complexity Minimalization of Nonsingleton-based Fuzzy-Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.4 No.4, pp. 286-293, 2000.Data files:
