DocumentCode
3381209
Title
Complexity reduction to non-singleton fuzzy-neural network
Author
Kóczy, Annamária R Várkonyi ; Lei, Kin-fong ; Sugiyama, Masaharu ; Asai, Hirotsugu
Author_Institution
Japanese-Hungarian Lab., Budapest Univ. of Technol. & Econ., Hungary
fYear
2001
fDate
25-28 July 2001
Firstpage
2523
Abstract
A singular value decomposition (SVD) based reduction technique has been proposed for a singleton-based fuzzy neural network. In fuzzy theory, the use of the non-singleton consequent-based Takagi-Sugeno model is also adopted. By applying a non-singleton-based fuzzy model to fuzzy neural networks, a non-singleton-based network is obtained. The main objective of this work is to extend the SVD-based reduction technique that has been proposed for fuzzy neural networks to non-singleton-based networks
Keywords
computational complexity; fuzzy neural nets; singular value decomposition; complexity reduction; fuzzy theory; nonsingleton consequent-based Takagi-Sugeno model; nonsingleton fuzzy neural networks; singular value decomposition; singular value-based reduction technique; Approximation algorithms; Computer networks; Electronic mail; Fuzzy logic; Fuzzy neural networks; Laboratories; Neural networks; Neurons; Takagi-Sugeno model; Transfer functions;
fLanguage
English
Publisher
ieee
Conference_Titel
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-7078-3
Type
conf
DOI
10.1109/NAFIPS.2001.943619
Filename
943619
Link To Document