The Research on BP Neural Network Model Based on Guaranteed Convergence Particle Swarm Optimization

In applying neural network to identification, back propagation (BP) algorithm is usually trapped to a local optimum and has a low speed of convergence, whereas particle swarm optimization (PSO) is advantageous in terms of global optimal searching. In this paper, a new algorithms which combines guaranteed convergence particle swarm optimizer (GCPSO) algorithm with BP (Back Propagation) algorithm is introduced in this paper and applied to BP neural networks to optimize the parameters of BP neural networks so as to improve the convergence speed and precision of BP neural networks. Compared with the BP algorithm and PSO-BP algorithm, the results of the simulation show that the algorithm of BP Neural Network Model Based on Guaranteed Convergence Particle Swarm Optimization speeds up the convergence process and enhances the accurate rate in pattern recognition.