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Classification using adaptive fuzzy inference neural networks

Jahankhani, Pari and Kodogiannis, Vassilis and Revett, Kenneth and Lygouras, John N. (2008) Classification using adaptive fuzzy inference neural networks. In: Proceedings of the Twelfth IASTED International Conference on Artificial Intelligence and Soft Computing, September 1-3 2008, Palma de Mallorca, Spain. ACTA Press, pp. 1-6. ISBN 9780889867550

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Abstract

The architecture and learning scheme of a novel fuzzy logic system implemented in the framework of a neural network is proposed. The network constructs its initial rules by clustering while the final fuzzy rule base is determined by competitive learning. Both error backpropagation and recursive least squares estimation, are applied to the learning scheme. The convergence of training is faster because the least-squares algorithm is applied to the estimation of consequence parameters of the system and backpropagation is applied only to the estimation of the premise parameters. Using the proposed scheme, high-dimensional fuzzy systems can be realized with fewer rules than a typical Takagi-Sugeno fuzzy system. A number of simulations demonstrate the performance of the proposed system.

Item Type:Book Section
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:8850
Deposited On:11 Nov 2010 12:29
Last Modified:11 Nov 2010 12:29

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