Optimization of fuzzy model using genetic algorithm for process control application

被引：32
作者：
Yusof, Rubiyah [2 ]
Rahman, Ribhan Zafira Abdul [1 ]
Khalid, Marzuki [2 ]
Ibrahim, Mohd Faisal [3 ]
机构：
[1] Univ Putra Malaysia, Dept Elect & Elect, Fac Engn, Serdang 43400, Selangor, Malaysia
[2] Univ Teknol Malaysia, Ctr Artificial Intelligence & Robot CAIRO, Kuala Lumpur 54100, Malaysia
[3] Univ Kebangsaan Malaysia, Dept Elect Elect & Syst Engn, Fac Engn, Bangi 43600, Selangor, Malaysia
来源：
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2011年 / 348卷 / 07期
关键词：
SYSTEMS;
D O I：
10.1016/j.jfranklin.2010.10.004
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A technique for the modeling of nonlinear control processes using fuzzy modeling approach based on the Takagi Sugeno fuzzy model with a combination of genetic algorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzy model. For the antecedent fuzzy parameters, genetic algorithm is used to tune them while at the consequent part, recursive least squares approach is used to identify the system parameters. This approach is applied to a process control rig with three subsystems: a heating element, a heat exchanger and a compartment tank. Experimental results show that the proposed approach provides better modeling when compared with Takagi Sugeno fuzzy modeling technique and the linear modeling approach. (C) 2010 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：1717 / 1737
页数：21
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