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

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Prasad, Girijesh, Leng, Gang, McGinnity, TM and Coyle, Damien (2010) On-line Identification of Self-organizing Fuzzy Neural Networks for Modelling Time-varying Complex Systems. In: Evolving Intelligent Systems: Methodology and Applications, IEEE Press Series on Computational Intelligence. (Eds: Angelov , Plamen, Filev , Dimitar and Kasabov, Nik), John Wiley & Sons, pp. 256-296. ISBN 0-470-28719-5 [Book section]

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URL: http://www.wiley-vch.de/publish/en/books/forthcomingTitles/EE00/0-470-28719-5/?sID=b89813f6a74d10df48dfd51950957034

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.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:12206
Deposited By:Professor Girijesh Prasad
Deposited On:09 Mar 2010 12:05
Last Modified:30 Aug 2011 12:24

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