CMAC neural network model based on compose particle swarm optimization

被引：0

作者：

Man Chun-tao ^{[1
]}

Wang Su-ju ^{[1
]}

Zhang Li-yong ^{[2
]}

机构：

[1] Harbin Univ Sci & Technol, Sch Automat, Harbin 150080, Peoples R China

[2] Harbin Univ Sci & Technol, Sch Measurement Control Tech & Commun Engn, Harbin 150080, Peoples R China

来源：

CIS WORKSHOPS 2007: INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY WORKSHOPS | 2007年

关键词：

D O I：

10.1109/CIS.Workshops.2007.176

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In order to improve the training precision of traditional CAMC model, this paper suggests anew CMAC model, whose weights are trained by composite particle swarm optimization. Traditional model's weights are trained by LMS algorithm, which can't learn approaching function's reciprocal and unfitted nonlinear hyperplane. The new method makes full use of the disadvantages of swarm intelligence, and improves above disadvantages effectively.

引用

页码：212 / +

页数：2

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