research-article

Dynamic fuzzy neural networks-a novel approach to function approximation

Authors:

Shiqian Wu and

Meng Joo ErAuthors Info & Claims

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Volume 30, Issue 2

Pages 358 - 364

https://doi.org/10.1109/3477.836384

Published: 01 April 2000 Publication History

Abstract

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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Li KQiao JWang D(2024)Fuzzy Stochastic Configuration Networks for Nonlinear System ModelingIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.331536832:3(948-957)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3315368
Salimi-Badr A(2024)A data-driven implicit deep adaptive neuro-fuzzy inference system capable of manifold learning for function approximationApplied Soft Computing10.1016/j.asoc.2024.111458155:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111458
Jin JGarg HYou T(2023)Generalized picture fuzzy distance and similarity measures on the complete lattice and their applicationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.119710220:COnline publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.119710
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Index Terms

Dynamic fuzzy neural networks-a novel approach to function approximation
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics Volume 30, Issue 2

April 2000

126 pages

ISSN:1083-4419

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IEEE Press

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Published: 01 April 2000

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Li KQiao JWang D(2024)Fuzzy Stochastic Configuration Networks for Nonlinear System ModelingIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2023.331536832:3(948-957)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1109/TFUZZ.2023.3315368
Salimi-Badr A(2024)A data-driven implicit deep adaptive neuro-fuzzy inference system capable of manifold learning for function approximationApplied Soft Computing10.1016/j.asoc.2024.111458155:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111458
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Cartagena OOžbot MSáez DŠkrjanc I(2023)Evolving fuzzy prediction interval for fault detection in a heat exchangerApplied Soft Computing10.1016/j.asoc.2023.110625145:COnline publication date: 1-Sep-2023
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Zhou HLi YZhang QXu HSu Y(2022)Soft-sensing of effluent total phosphorus using adaptive recurrent fuzzy neural network with Gustafson-Kessel clusteringExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117589203:COnline publication date: 1-Oct-2022
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Zhou HLi YXu HSu YChen L(2022)A self-organizing fuzzy neural network modeling approach using an adaptive quantum particle swarm optimizationApplied Intelligence10.1007/s10489-022-04133-853:11(13569-13592)Online publication date: 13-Oct-2022
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