Particle Swarm Optimization Based Learning Algorithm for Process Neural Networks

This paper proposes a new learning algorithm for process neural networks(PNNs) based on particle swarm optimization(PSO),called PSO-LM.After the orthogonal basis function expansion to the input functions and the weight functions of the PNN,the structure parameters and other parameters in the PNN will be formed as a particle,and globally optimized by PSO.This algorithm does not need any gradient calculations or the manual control of the network's structure.The global learning capability and the convergence capability of the PNN can be guaranteed by the capabilities of PSO,so the PSO-LM can better develop and improve the approximation capability of the PNN.According to two practical prediction applications,PSO-LM can outperform the existing basis function expansion based learning algorithm(BFE-LM) for PNNs,and the classic back propagation neural networks(BPNNs) on predictive accuracy.