title: Lattice dynamical wavelet neural networks implemented using particle swarm optimization for spatio-temporal system identification.
abstract: 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.
authors.affiliation: Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield S1 3JD, UK. w.hualiang@sheffield.ac.uk
authors.forename: Hua-Liang
authors.initials: HL
authors.lastname: Wei
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Billings
authors.forename: Yifan
authors.initials: Y
authors.lastname: Zhao
authors.forename: Lingzhong
authors.initials: L
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D016571:Neural Networks, Computer; D009474:Neurons; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D012044:Regression Analysis; D013997:Time Factors