Dynamic fuzzy neural networks-a novel approach to function approximation

In this paper, an architecture of dynamic fuzzy neural networks (D-FNN) implementing Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended radial basis function (RBF) neural networks is proposed. A novel learning algorithm based on D-FNN is also presented. The salient characteristics of the algor...

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Bibliographic Details
Published inIEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 30; no. 2; pp. 358 - 364
Main Authors Wu, S, Er, M J
Format Journal Article
LanguageEnglish
Published United States IEEE 01.04.2000
Subjects
Algorithms
Backpropagation algorithms
Computer simulation
Dynamical systems
Dynamics
Erbium
Function approximation
Fuzzy
Fuzzy logic
Fuzzy neural networks
Fuzzy systems
Learning
Modeling
Neural networks
Neurons
Recruitment
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Summary:In this paper, an architecture of dynamic fuzzy neural networks (D-FNN) implementing Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended radial basis function (RBF) neural networks is proposed. A novel learning algorithm based on D-FNN is also presented. The salient characteristics of the algorithm are: 1) hierarchical on-line self-organizing learning is used; 2) neurons can be recruited or deleted dynamically according to their significance to the system's performance; and 3) fast learning speed can be achieved. Simulation studies and comprehensive comparisons with some other learning algorithms demonstrate that a more compact structure with higher performance can be achieved by the proposed approach.
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ISSN:1083-4419
1941-0492
DOI:10.1109/3477.836384