Particle Swarm Optimization Based Learning Method for Process Neural Networks

Abstract

This paper proposes a new learning method for process neural networks (PNNs) based on the Gaussian mixture functions and particle swarm optimization (PSO), called PSO-LM. First, the weight functions of the PNNs are specified as the generalized Gaussian mixture functions (GGMFs). Second, a PSO algorithm is used to optimize the parameters, such as the order of GGMFs, the number of hidden neurons, the coefficients, means and variances of Gaussian functions and the thresholds in PNNs. In PSO-LM, the parameter space is transformed from the function space to real number space by using GGMFs. PSO can give a global search in the real number parameter space by avoiding the premature and gradient calculations in back propagation method. According to our analysis and several experiments, PSO-LM can outperform current basis function expansion based learning method (BFE-LM) for PNNs and the classic back propagation neural networks (BPNNs).