An adaptive wavelet neural network for spatio-temporal system identification

作者：Wei H L; Billings S A^*; Zhao Y F; Guo L Z

来源：Neural Networks, 2010, 23(10): 1286-1299.

DOI：10.1016/j.neunet.2010.07.006

• Coupled map lattices
• Neural networks
• Particle swarm optimization
• Spano temporal systems
• Wavelets

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 out

• 出版日期2010-12