摘要
针对网络控制系统存在的随机时延问题,本文基于BP神经网络(Back Propagation Neural Network, BPNN)建模方法,在PSO(Particle Swarm Optimization)算法的基础上引入遗传算法中交叉和变异的思想,同时对惯性权重和学习因子采用线性递减和异步时变的改进策略,提出了一种性能更优的改进PSO算法,并用该算法优化BP神经网络,构建了一种改进PSO-BP神经网络的时延预测模型;然后运用MATLAB TrueTime2.0工具箱搭建仿真平台,结合获取到的历史时延采样数据对改进PSO-BP时延预测模型和PSO-BP、BP模型进行性能对比测试.实验表明本文所提出模型的预测精度更高,误差更小,能较好的解决网络控制系统的随机时延预测问题.
To address the problem of stochastic delay in network control systems,this paper introduces the idea of crossover and variation in genetic algorithm based on the PSO algorithm,and uses a linear decreasing and asynchronous time-varying improvement strategy for inertia weights and The improved PSO algorithm with better performance is proposed,and the BP neural network is optimized with this algorithm to construct an improved PSO-BP neural network delay prediction model;then the MATLAB TrueTime2.0 toolbox is used to build a simulation platform and combine the obtained historical delay sampling data to improve the PSO-BP neural network delay prediction model and the PSO-BP neural network delay prediction model.BP delay prediction model and PSO-BP,BP model for performance comparison test.The experiments show that the proposed model has higher prediction accuracy and smaller error,which can better solve the stochastic delay prediction problem of network control system.
作者
魏天旭
赵燕成
赵景波
胡阵
WEI Tian-xu;ZHAO Yan-cheng;ZHAO Jing-bo;HU Zhen(School of Information and Control Engineering,Qingdao University of Technology,Qingdao 266520,China)
出处
《陕西科技大学学报》
北大核心
2024年第3期158-165,173,共9页
Journal of Shaanxi University of Science & Technology
基金
国家自然科学基金项目(51475251)
山东省重点研发计划项目(2023RZA02017)
山东省青岛市民生计划项目(22-3-7-xdny-18-nsh)。
关键词
网络控制系统
PSO算法
BP神经网络
网络诱导时延
时延预测
network control system
particle swarm algorithm
BP neural network
network induced delay
time delay prediction