Soft-sensor Modeling of Rectification of Vinyl Chloride Based on Improved PSO-RBF Neural Network
For the purity of vinyl chloride distillation process difficultly on-line detective timely, a strategy of vinyl chloride purity soft measurement modeling based on particle swarm optimization Improved RBF neural network is proposed.Firstly, we combine the PSO algorithm with RBF neural network to optimize RBF structure parameter. Then, vinyl chloride purity soft measurement modeling and optimization is realized.Lastly, conducted a simulation verification.In the end, simulation results show that the soft measurement model has a faster convergence speed, a higher approximation accuracy,and a stronger real-time prediction ability.
rectification of vinyl chloride soft-sensor RBF neural network PSO particle swarm
GAO Shuzhi SUN Jie GAO Xianwen
Northeastern University, Shenyang 110004, China. Shenyang University of Chemical Technology, Shenyan Shenyang University of Chemical Technology, Shenyang 110142, China Northeastern University, Shenyang 110004, China.
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
1122-1126
2012-05-23(万方平台首次上网日期,不代表论文的发表时间)