Complexity reduction to non-singleton fuzzy-neural network

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 n...

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Bibliographic Details
Main Authors: Varkonyi-Koczy, A.R., Kin-fong Lei, Sugiyama, M., Asai, H.
Format: Conference Proceeding
Language:English
Subjects:
Approximation algorithms
Computer networks
Electronic mail
Fuzzy logic
Fuzzy neural networks
Laboratories
Neural networks
Neurons
Takagi-Sugeno model
Transfer functions
Online Access:Request full text
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Summary: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.
DOI:10.1109/NAFIPS.2001.943619