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Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification

Wei, H.L., Billings, S.A., Zhao, Y. and Guo, L. (2009) Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification. IEEE Transactions on Neural Networks, 20 (1). pp. 181-185. ISSN 1045-9227

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In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel two-stage hybrid training scheme is developed for constructing a parsimonious network model. In the first stage, by applying the OPP algorithm, significant wavelet neurons are adaptively and successively recruited into the network, where adjustable parameters of the associated wavelet neurons are optimized using a particle swarm optimizer. The resultant network model, obtained in the first stage, however, may be redundant. In the second stage, an orthogonal least squares algorithm is then applied to refine and improve the initially trained network by removing redundant wavelet neurons from the network. An example for a real spatio-temporal system identification problem is presented to demonstrate the performance of the proposed new modeling framework.

Item Type: Article
Copyright, Publisher and Additional Information: © 2009 Institute of Electrical and Electronics Engineers. This is an author produced version of a paper subsequently published in IEEE Transactions on Neural Newtworks. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Coupled map; lattice, neural networks, particle swarm optimization (PSO), spatio–temporal systems, wavelets
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: 30 Apr 2009 10:22
Last Modified: 15 Sep 2014 01:38
Published Version: http://dx.doi.org/10.1109/TNN.2008.2009639
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: 10.1109/TNN.2008.2009639
URI: http://eprints.whiterose.ac.uk/id/eprint/8553

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