The Kalman particle swarm optimization algorithm and its application in soft-sensor of acrylonitrile yield

被引：0
|
作者：
Xu, Yufa [1 ]
Chen, Guochu
Yu, Jinshou
机构：
[1] E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China
[2] Shanghai DianJi Univ, Coll Elect Engn, Shanghai 200240, Peoples R China
来源：
ADVANCES IN NATURAL COMPUTATION, PT 2 | 2006年 / 4222卷
关键词：
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 kalman particle swarm optimization algorithm (KPSO), which combines kalman filter with PSO. Comparison of optimization performance between KPSO and PSO with three test functions shows that KPSO has better optimization performance than PSO. The combination of KPSO and ANN is also introduced (KPSONN). Then, KPSONN is applied to construct a soft-sensor of acrylonitrile yield. After comparing with practical industrial data, the result shows that KPSONN is feasible and effective in soft-sensor of acrylonitrile yield.
引用
收藏
页码：176 / 179
页数：4
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