### Abstract

Language | English |
---|---|

Title of host publication | Evolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence |

Editors | Plamen Angelov, Dimitar Filev, Nik Kasabov |

Pages | 256-296 |

Publication status | Published - 2010 |

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### Cite this

*Evolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence*(pp. 256-296)

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*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.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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

A2 - Kasabov, Nik

ER -