A self-organizing neural network using hierarchical particle swarm optimization

This paper introduces a hierarchical particle swarm optimization (HPSO) algorithm strategy for self-organizing neural network design. The proposed CHPSO can determine the structure of the neural network and tune the parameters in the neural network automatically. The structure learning is based on the genetic algorithm (GA) and the parameter learning is based on the particle swarm optimization (PSO). The advantages of the proposed learning algorithm can obtain fine structure and performance for neural network (NN). The prediction of simulation example has been given to illustrate the performance and effectiveness of the proposed model.