[1]段艳明. 基于PSO算法和BP神经网络的PID控制研究[J].计算机技术与发展,2014,24(08):238-241.
　DUAN Yan-ming. Research of PID Control Based on BP Neural Network and PSO Algorithm[J].,2014,24(08):238-241.
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 基于PSO算法和BP神经网络的PID控制研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年08期
页码:
238-241
栏目:
应用开发研究
出版日期:
2014-08-10

文章信息/Info

Title:
 Research of PID Control Based on BP Neural Network and PSO Algorithm
文章编号:
1673-629X（2014）08-0238-04
作者:
 段艳明
 河池学院 计算机与信息工程学院
Author(s):
 DUAN Yan-ming
关键词:
 ; PID控制; BP神经网络; PSO算法
Keywords:
 ; PID control; BP neural network; PSO algorithm
分类号:
TP391.9
文献标志码:
A
摘要:
 针对PID控制中的参数整定的难点及基本BP算法收敛速度慢、易陷入局部极值的问题，提出利用PSO算法的全局寻优能力和较强的收敛性来改进BP网络的权值调整新方法，从而对PID控制的比例、积分、微分进行优化控制。该方法是在基本BP算法的误差反向传播的基础上，使粒子位置的更新对应BP网络的权值和阈值的调整，既充分利用了PSO算法的全局寻优性又较好地保持了BP算法本身的反向传播特点。仿真结果表明基于PSO算法的BP神经网络的PID优化控制具有较好的性能和自学习、自适应性。
Abstract:
 In view of the difficulty of parameters setting of PID control and the limitations of slow convergence and local extreme values of BP algorithm,a new method to adjust weights of BP network is proposed using the global optimization ability and the strong conver-gence by PSO algorithm,so as to optimize the proportional,integral and differential of PID control. The new algorithm is based on the weight adjustments of error back propagation of BP algorithm,making the bats position updating to weight and threshold of BP network modification. The new algorithm can not only use the global optimization of PSO algorithm,but also contain the feature of error back propagation of BP algorithm. Experimental results show that the PID optimization control based on BP neural network has better perform-ance and self learning and adaptive.

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更新日期/Last Update: 2015-03-31