附加动量在PID神经网络中的应用

Huailin Shu, Yuan Xu
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引用次数: 8

摘要

PID神经网络(PID neural network, PIDNN)是舒怀林提出的将神经网络与PID控制律相结合的一种新型前馈神经网络。利用PID控制的先验知识来选择初始权值,以保证PID神经网络控制系统的初始稳定。在没有先验知识和使用随机初始权值的情况下,PIDNN系统的初始稳定性可能是不确定的。本文提出了一种带有动量因子的改进算法来克服PIDNN的局部最小值问题。仿真结果证明了PIDNN控制系统的显著改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of additional momentum in PID neural network
PID neural network (PIDNN) is a new type of feed-forward neural network which has been found by Huai-lin Shu, in which neural network is integrated with PID control law. The priori knowledge of PID control can be used to choose the initial weights to make sure that PIDNN control systems are initial stable. Without priori knowledge and using the random initial weights, initial stable of the PIDNN system may be uncertain. The paper proposes an improved algorithm which has momentum factor to overcome the local minimum of the PIDNN. The distinct improvement of the PIDNN control system is proved by simulation results.
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