摘要
针对网络控制系统时延的随机、时变、非线性等特性,提出了基于粒子群优化的BP神经网络的预测方法。对实测时延数据样本进行归一化处理,以BP神经网络误差的平方和作为粒子群优化算法的适应值函数;采用粒子群算法优化BP神经网络的初始权值和阈值;将粒子群算法中全局最优值输出作为BP神经网络的初始权值和阈值对时延样本数据进行训练预测。仿真表明,该时延预测算法的迭代次数减少,同时避免算法陷入极值点,预测精度更好。
Aiming at the random,time-varying and nonlinear characteristics of network control system delay,a prediction method of BP neural network based on particle swarm optimization is proposed.Normalize the collected time delay data samples,and use the squared sum of the errors of the BP neural network as the fitness value function in the PSO algorithm.Use the PSO algorithm to optimize the initial weight and threshold of the BP neural network.The PSO algorithm The global optimal value output is used as the initial weight and threshold of the BP neural network to train and predict the delay sample data.The simulation results show that the number of iterations of the algorithm is reduced,and the algorithm can avoid falling into the extreme point,so the prediction accuracy is better.
作者
时维国
雷何芬
SHI Wei-guo;LEI He-fen(School of Electrical Information Engineering,Dalian Jiaotong University,Dalian 116028,China)
出处
《自动化与仪表》
2020年第7期1-5,共5页
Automation & Instrumentation
基金
辽宁省自然科学基金项目(20170540141)。
关键词
粒子群优化
BP神经网络
网络控制
时延预测
particle swarm optimization(PSO)
BP neural network
network control
delay prediction
