改进PSO算法的BP网络对泵控马达系统的优化

摘要

Aiming at how to improve BP network disadvantages that is easy to fall into local minimum and slow conver-gence, a multi group with variation of grey operator Collaborative Chaotic Particle Swarm Optimization algorithm (GMMCCPSO)is proposed. The gray mutation operator is applied to multi-group cooperative particle swarm main group in order to avoid the phenomenon that is premature local convergence of the main group. In order to enhance the capacity of local search from group, the chaos theory is introduced from each group. Using improved particle swarm optimization algorithm to optimize BP neural network weights and thresholds, the shortcoming of the BP network that is easy to fall into local minima and slow convergence of shortcomings is effectively improved, and it also greatly improves its mapping capabilities. By the study of controlling the pump motor system simulation of MATLAB, the results show that the improved PSO-BP network effectively improves the shortcomings of the system about the ability to identify the presence of sudden load, the oscillation and the response speed being poor of the system.%针对如何有效改善BP网络易陷于局部极小和收敛速度慢的缺点，提出了一种带有变异灰色算子的多群体协同混沌粒子群算法（GMMCCPSO）。将灰色变异算子应用于多群体协同粒子群的主群，以避免主群过早出现局部收敛现象；将混沌理论引入各从群，以增强各从群的局部搜索能力。利用改进的粒子群算法来优化BP神经网络的权值和阈值，有效地改善了BP网络易陷入局部极小和收敛慢的缺点，同时也极大地提高了其映射能力。通过对泵控马达系统进行MATLAB仿真研究，结果表明：改进的PSO-BP网络有效地改善了该系统存在对突加负载的识别能力、系统振荡性和响应速度差的缺点。