A hybrid optimization method based on cellular automata and its application in soft-sensing Modeling

被引：0
|
作者：
Xu, Yufa [1 ,2 ]
Chen, Guochu [1 ]
Yu, Jinshou [2 ]
机构：
[1] Shanghai DianJi Univ, Sch Elect Engn, Shanghai 200240, Peoples R China
[2] East China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China
来源：
ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 3, PROCEEDINGS | 2007年
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：
By studying cellular automata, a new optimization method based on cellular automata is proposed by this paper. The new optimization method assumes that "life game" are applied in operator of genetic algorithm (G,4). Experiment results show that the new method has good optimization performance. Then, a hybrid neural network algorithm based on life game, G,4 and back-propagation algorithm is presented to train soft-sensing model of acrylonitrile yield. Experiment results show that the hybrid soft sensing model proposed in this paper has good performance and high measuring precision.
引用
收藏
页码：231 / +
页数：2
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