河北科技大学学报2017，Vol.38Issue(6)：578-584,7.DOI:10.7535/hbkd.2017yx06011
改进的PSO-RBF神经网络在联合制碱中的应用
Application of improved PSO-RBF neural network in the synthetic ammonia decarbonization
摘要
Abstract
The synthetic ammonia decarbonization is a typical complex industrial process,which has the characteristics of time variation,nonlinearity and uncertainty,and the on-line control model is difficult to be established.An improved PSO-RBF neu-ral network control algorithm is proposed to solve the problems of low precision and poor robustness in the complex process of the synthetic ammonia decarbonization.The particle swarm optimization algorithm and RBF neural network are combined.The improved particle swarm algorithm is used to optimize the RBF neural network's hidden layer primary function center,width and the output layer's connection value to construct the RBF neural network model optimized by the improved PSO algorithm. The improved PSO-RBF neural network control model is applied to the key carbonization process and compared with the tradi-tional fuzzy neural network.The simulation results show that the improved PSO-RBF neural network control method used in the synthetic ammonia decarbonization process has higher control accuracy and system robustness,which provides an effective way to solve the modeling and optimization control of a complex industrial process.关键词
自动化技术应用/联合制碱/粒子群优化算法/RBF神经网络/优化控制Key words
automated technology applications/the synthetic ammonia decarbonization/PSO/RBF neural network/optimal control分类
信息技术与安全科学引用本文复制引用
李永伟,李钰曼,王红飞,李丽铭..改进的PSO-RBF神经网络在联合制碱中的应用[J].河北科技大学学报,2017,38(6):578-584,7.基金项目
河北省自然科学基金(F2014208145) （F2014208145）
