The Kalman Particle Swarm Optimization Algorithm and its application in soft-sensor of acrylonitrile yield

被引：0
|
作者：
Guo, W [1 ]
Chen, GC [1 ]
Yu, JS [1 ]
机构：
[1] E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China
来源：
PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3 | 2005年
关键词：
KPSO; PSO; acrylonitrile yield; soft-sensor;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposes the Kalaman Particle Swarm Optimization Algorithm (KPSO), which combines the Kalman Filter and PSO. KPSO assumes that particle moves according to the Kalman Filer. The comparison of optimization performance between KPSO and PSO to three widely used test functions shows that the optimization performance of KPSO is much better than that of PSO. The combination of KPSO and ANN is also introduced (KPSONN). Then, KPSONN is applied to construct a practical soft-sensor of acrylonitrile yield. After comparing with practical industrial data, the obtained result shows that the KPSONN is feasible and effective in soft-sensing of acrylonitrile yield.
引用
收藏
页码：124 / 127
页数：4
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