Training Wavelet Neural Networks Using Hybrid Particle Swarm Optimization and Gravitational Search Algorithm for System Identification

system identification is mainly the process of improving a mathematical modeling of a physical system using experimental data. In this paper, a new hybrid wavelet neural network is proposed for the system identification purposes. The Gravitational Search Algorithm (GSA) is a new evolutionary algorithm which recently introduced and has a good performance in different optimization problems. The GSA inspired by the law of gravity and mass interactions. The only disadvantage of GSA is that suffers from slow searching speed in the last iterations. In this paper the hybridization of the defined algorithms (GSAPSO) is proposed for constructing and training wavelet neural networks. The difference of the conventional neural network and wavelet neural network is that the activation function of the original WNN is based on wavelet transformation. This algorithm is based on the optimal selection of network weights dynamically during the training process. The suggested method determines the optimal value of the weights and solves the optimization problem of wavelet neural network structure. The problem of finding a good neural model is then discussed through solutions achieved by wavelet neural networks trained by PSO based and GSA based algorithms. Experimental results show that this method can improve the performance of the wavelet based neural network significantly.

Neural Network, Wavelet, Learning neural network, Gravitational search algorithm, Particle swarm optimization, System Identification, Nonlinear System.