计算机技术与发展
計算機技術與髮展
계산궤기술여발전
COMPUTER TECHNOLOGY AND DEVELOPMENT
2014年
8期
238-241
,共4页
PID控制%BP神经网络%PSO算法
PID控製%BP神經網絡%PSO算法
PID공제%BP신경망락%PSO산법
PID control%BP neural network%PSO algorithm
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。
針對PID控製中的參數整定的難點及基本BP算法收斂速度慢、易陷入跼部極值的問題，提齣利用PSO算法的全跼尋優能力和較彊的收斂性來改進BP網絡的權值調整新方法，從而對PID控製的比例、積分、微分進行優化控製。該方法是在基本BP算法的誤差反嚮傳播的基礎上，使粒子位置的更新對應BP網絡的權值和閾值的調整，既充分利用瞭PSO算法的全跼尋優性又較好地保持瞭BP算法本身的反嚮傳播特點。倣真結果錶明基于PSO算法的BP神經網絡的PID優化控製具有較好的性能和自學習、自適應性。
침대PID공제중적삼수정정적난점급기본BP산법수렴속도만、역함입국부겁치적문제，제출이용PSO산법적전국심우능력화교강적수렴성래개진BP망락적권치조정신방법，종이대PID공제적비례、적분、미분진행우화공제。해방법시재기본BP산법적오차반향전파적기출상，사입자위치적경신대응BP망락적권치화역치적조정，기충분이용료PSO산법적전국심우성우교호지보지료BP산법본신적반향전파특점。방진결과표명기우PSO산법적BP신경망락적PID우화공제구유교호적성능화자학습、자괄응성。
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.
