An adaptive wavelet neural network for spatio-temporal system identification

Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks (AWNN) is introduced for spatio-temporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. A new orthogonal projection pursuit (OPP) metho...

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Bibliographic Details
Published in:Neural networks 2010-12, Vol.23 (10), p.1286-1299
Main Authors: Wei, H.L., Billings, S.A., Zhao, Y.F., Guo, L.Z.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Computer Simulation
Connectionism. Neural networks
Control theory. Systems
Coupled map lattices
Exact sciences and technology
Modelling and identification
Models, Biological
Neural networks
Neural Networks (Computer)
Particle swarm optimization
Spatio-temporal systems
Wavelets
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Summary:Starting from the basic concept of coupled map lattices, a new family of adaptive wavelet neural networks (AWNN) is introduced for spatio-temporal system identification, by combining an efficient wavelet representation with a coupled map lattice model. 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 orthogonal projection pursuit 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, may however 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. The proposed two-stage hybrid training procedure can generally produce a parsimonious network model, where a ranked list of wavelet neurons, according to the capability of each neuron to represent the total variance in the system output signal is produced. Two spatio-temporal system identification examples are presented to demonstrate the performance of the proposed new modelling framework.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2010.07.006