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Generalised cellular neural networks (GCNNs) constructed using particle swarm optimisation for spatio-temporal evolutionary pattern identification

Wei, H.L. and Billings, S.A. (2008) Generalised cellular neural networks (GCNNs) constructed using particle swarm optimisation for spatio-temporal evolutionary pattern identification. International Journal of Bifurcation and Chaos, 18 (12). pp. 3611-3624. ISSN 0218-1274

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Abstract

Particle swarm optimization (PSO) is introduced to implement a new constructive learning algorithm for training generalized cellular neural networks (GCNNs) for the identification of spatio-temporal evolutionary (STE) systems. The basic idea of the new PSO-based learning algorithm is to successively approximate the desired signal by progressively pursuing relevant orthogonal projections. This new algorithm will thus be referred to as the orthogonal projection pursuit (OPP) algorithm, which is in mechanism similar to the conventional projection pursuit approach. A novel two-stage hybrid training scheme is proposed for constructing a parsimonious GCNN model. In the first stage, the orthogonal projection pursuit algorithm is applied to adaptively and successively augment the network, where adjustable parameters of the associated units are optimized using a particle swarm optimizer. The resultant network model produced at the first stage may be redundant. In the second stage, a forward orthogonal regression (FOR) algorithm, aided by mutual information estimation, is applied to re. ne and improve the initially trained network. The effectiveness and performance of the proposed method is validated by applying the new modeling framework to a spatio-temporal evolutionary system identification problem.

Item Type: Article
Copyright, Publisher and Additional Information: Electronic version of an article published as, International Journal of Bifurcation and Chaos, 18, 12, 2008, 3611-3624 DOI: 10.1142/S0218127408022585 © copyright World Scientific Publishing Company http://www.worldscinet.com/ijbc/ijbc.shtml
Keywords: Cellular neural networks, coupled map lattices, evolutionary algorithms, mutual information, neural networks, orthogonal least squares, parameter estimation, particle swarm optimization, spatio-temporal evolutionary systems
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Miss Anthea Tucker
Date Deposited: 12 May 2009 12:48
Last Modified: 08 Feb 2013 16:58
Published Version: http://dx.doi.org/10.1142/S0218127408022585
Status: Published
Publisher: World Scientific Publishing
Refereed: Yes
Identification Number: 10.1142/S0218127408022585
URI: http://eprints.whiterose.ac.uk/id/eprint/8551

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