Generalized Cellular Neural Networks (Gcnns) Constructed Using Particle Swarm Optimization for Spatio-Temporal Evolutionary Pattern Identification

Particle swarm optimisation (PSO) is introduced to implement a new constructive learning algorithm for training generalised cellular neural networks (GCNNs) for the identification of spatiotemporal 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 optimised using a particle swarm optimiser. 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 refine and improve the initially trained network. The effectiveness and performance of the proposed method is validated by applying the new modelling framework to a spatio-temporal evolutionary system identification problem.