计算机与现代化Issue(3)：71-74,79,5.DOI:10.3969/j.issn.1006-2475.2015.03.015
基于粒子群算法的抄纸过程PID神经元网络优化控制
PID Neural Network Optimizing Control Based on Particle Swarm Optimization in Paper Process
摘要
Abstract
The optimal control of basis weight and moisture content in paper process with strong coupling, nonlinear and large time delay is difficult to achieve. To solve the problem, the optimal PID neural network controller by particle swarm optimization was adopted in the control system. Because the network structure was simple and a modified error back propagation algorithm with momentum factor was used, the learning speed was increased and the reaction time of the system became short. Particle swarm optimization was used to optimize the initial weights of PID neural network to avoid local optimization for obtaining better control accuracy. Simulation results show PID neural network optimizated by the network’ s initial weights is of better adaptability, de-coupling ability and robustness in the decoupling control of basis weight and moisture content. It is a new method for the control of basis weight and moisture content in paper process.关键词
粒子群算法/PID神经元网络/优化/抄纸过程Key words
particle swarm optimization( PSO)/PID neural network/optimization/paper process分类
信息技术与安全科学引用本文复制引用
吴新生..基于粒子群算法的抄纸过程PID神经元网络优化控制[J].计算机与现代化,2015,(3):71-74,79,5.基金项目
广东省自然科学基金资助项目(8451064007000003) （8451064007000003）
