On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Fuzzy neural networks are hybrid systems that combine the theories of fuzzy logic and neural networks. By incorporating in these hybrid systems the ability to self-organize their network structure, self-organizing fuzzy neural networks (SOFNNs) are created. The SOFNNs have enhanced ability to identify adaptive models, mainly for representing nonlinear and time-varying complex systems, where little or insufficient expert knowledge is available to describe the underlying behavior. Problems that arise in these systems are large dimensions, time-varying characteristics, large amounts of data and noisy measurements, as well as the need for an interpretation of the resulting model. This chapter presents an algorithm for on-line identification of a self-organizing fuzzy neural network (SOFNN). The SOFNN provides a singleton or Takagi-Sugeno (TS) type fuzzy model. It therefore facilitates extracting fuzzy rules from the training data. The algorithm is formulated to guarantee the convergence of both the estimation error and the linear network parameters. It generates a fuzzy neural model with a high accuracy and compact structure. Superior performance of the algorithm is demonstrated through its applications for function approximation, system identification, and time-series prediction in both industrial and biological systems.
LanguageEnglish
Title of host publicationEvolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence
EditorsPlamen Angelov, Dimitar Filev, Nik Kasabov
Pages256-296
Publication statusPublished - 2010

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Fuzzy neural networks
Large scale systems
Hybrid systems
Linear networks
Fuzzy rules
Biological systems
Error analysis
Fuzzy logic
Time series
Identification (control systems)
Neural networks

Cite this

Prasad, G., Leng, G., McGinnity, TM., & Coyle, D. (2010). On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems. In P. Angelov, D. Filev, & N. Kasabov (Eds.), Evolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence (pp. 256-296)
Prasad, Girijesh ; Leng, Gang ; McGinnity, TM ; Coyle, Damien. / On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems. Evolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence. editor / Plamen Angelov ; Dimitar Filev ; Nik Kasabov. 2010. pp. 256-296
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abstract = "Fuzzy neural networks are hybrid systems that combine the theories of fuzzy logic and neural networks. By incorporating in these hybrid systems the ability to self-organize their network structure, self-organizing fuzzy neural networks (SOFNNs) are created. The SOFNNs have enhanced ability to identify adaptive models, mainly for representing nonlinear and time-varying complex systems, where little or insufficient expert knowledge is available to describe the underlying behavior. Problems that arise in these systems are large dimensions, time-varying characteristics, large amounts of data and noisy measurements, as well as the need for an interpretation of the resulting model. This chapter presents an algorithm for on-line identification of a self-organizing fuzzy neural network (SOFNN). The SOFNN provides a singleton or Takagi-Sugeno (TS) type fuzzy model. It therefore facilitates extracting fuzzy rules from the training data. The algorithm is formulated to guarantee the convergence of both the estimation error and the linear network parameters. It generates a fuzzy neural model with a high accuracy and compact structure. Superior performance of the algorithm is demonstrated through its applications for function approximation, system identification, and time-series prediction in both industrial and biological systems.",
author = "Girijesh Prasad and Gang Leng and TM McGinnity and Damien Coyle",
year = "2010",
language = "English",
isbn = "0-470-28719-5",
pages = "256--296",
editor = "Plamen Angelov and Dimitar Filev and Nik Kasabov",
booktitle = "Evolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence",

}

Prasad, G, Leng, G, McGinnity, TM & Coyle, D 2010, On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems. in P Angelov, D Filev & N Kasabov (eds), Evolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence. pp. 256-296.

On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems. / Prasad, Girijesh; Leng, Gang; McGinnity, TM; Coyle, Damien.

Evolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence. ed. / Plamen Angelov; Dimitar Filev; Nik Kasabov. 2010. p. 256-296.

Research output: Chapter in Book/Report/Conference proceedingChapter

TY - CHAP

T1 - On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems

AU - Prasad, Girijesh

AU - Leng, Gang

AU - McGinnity, TM

AU - Coyle, Damien

PY - 2010

Y1 - 2010

N2 - Fuzzy neural networks are hybrid systems that combine the theories of fuzzy logic and neural networks. By incorporating in these hybrid systems the ability to self-organize their network structure, self-organizing fuzzy neural networks (SOFNNs) are created. The SOFNNs have enhanced ability to identify adaptive models, mainly for representing nonlinear and time-varying complex systems, where little or insufficient expert knowledge is available to describe the underlying behavior. Problems that arise in these systems are large dimensions, time-varying characteristics, large amounts of data and noisy measurements, as well as the need for an interpretation of the resulting model. This chapter presents an algorithm for on-line identification of a self-organizing fuzzy neural network (SOFNN). The SOFNN provides a singleton or Takagi-Sugeno (TS) type fuzzy model. It therefore facilitates extracting fuzzy rules from the training data. The algorithm is formulated to guarantee the convergence of both the estimation error and the linear network parameters. It generates a fuzzy neural model with a high accuracy and compact structure. Superior performance of the algorithm is demonstrated through its applications for function approximation, system identification, and time-series prediction in both industrial and biological systems.

AB - Fuzzy neural networks are hybrid systems that combine the theories of fuzzy logic and neural networks. By incorporating in these hybrid systems the ability to self-organize their network structure, self-organizing fuzzy neural networks (SOFNNs) are created. The SOFNNs have enhanced ability to identify adaptive models, mainly for representing nonlinear and time-varying complex systems, where little or insufficient expert knowledge is available to describe the underlying behavior. Problems that arise in these systems are large dimensions, time-varying characteristics, large amounts of data and noisy measurements, as well as the need for an interpretation of the resulting model. This chapter presents an algorithm for on-line identification of a self-organizing fuzzy neural network (SOFNN). The SOFNN provides a singleton or Takagi-Sugeno (TS) type fuzzy model. It therefore facilitates extracting fuzzy rules from the training data. The algorithm is formulated to guarantee the convergence of both the estimation error and the linear network parameters. It generates a fuzzy neural model with a high accuracy and compact structure. Superior performance of the algorithm is demonstrated through its applications for function approximation, system identification, and time-series prediction in both industrial and biological systems.

M3 - Chapter

SN - 0-470-28719-5

SP - 256

EP - 296

BT - Evolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence

A2 - Angelov, Plamen

A2 - Filev, Dimitar

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Prasad G, Leng G, McGinnity TM, Coyle D. On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems. In Angelov P, Filev D, Kasabov N, editors, Evolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence. 2010. p. 256-296