基于动量因子优化学习率的BP神经网络PID参数整定算法

针对传统BP神经网络学习过程中学习率选取过大导致振荡的问题, 提出一种新的BP神经网络PID(比例-积分-微分)参数自适应整定算法. 采用BP神经网络对PID参数进行自适应调节和优化, 并利用动量因子优化学习率和增加动量项抑制BP神经网络训练中出现的振荡现象, 以加快收敛速度. 实验结果表明, 该算法有效缓解了振荡现象, 加快了算法的收敛速度.

BP Neural Network PID Parameter Tuning Algorithm Based on Momentum Factor Optimized Learning Rate

Aiming at the problem of oscillation caused by excessive selection of learning rate in the learning process of traditional BP neural network, we proposed a new adaptive tuning algorithm for PID (proportional-integral-differential) parameters of BP neural network. BP neural network was used to adjust and optimize PID parameters adaptively, and momentum factor was used to optimize learning rate and increase momentum term to restrain oscillation phenomenon in BP neural network training, so as to accelerate convergence speed. The experimental results show that the proposed algorithm can effectively alleviate the oscillation phenomenon and accelerate the convergence speed of the algorithm.