Recursive least square and fuzzy modelling using genetic algorithm for process control application

被引：0
|
作者：
Rahman, Ribhan Zafira Abdul [1 ]
Yusof, Rubiyah [2 ]
Khalid, Marzuki [2 ]
机构：
[1] Univ Putra Malaysia, Fac Engn, Dept Elect & Elect, Serdang, Malaysia
[2] Univ Teknol Malaysia, Fac Elect Engn, Ctr Artificial Intelligence & Robot CAIRO, Johor Baharu, Malaysia
来源：
AMS 2007: FIRST ASIA INTERNATIONAL CONFERENCE ON MODELLING & SIMULATION ASIA MODELLING SYMPOSIUM, 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：
A technique for the modelling of nonlinear process control using Recursive Least Square and Takagi-Sugeno Fuzzy System with Genetic Algorithm topology is described. This paper discusses the identification of parameters of the fuzzy sets at the antecedent part and linear model at the consequent part of fuzzy model within an application to process control. The key issues of finding the best model of the process are described. Results show that fuzzy model with genetic algorithm gives minimum mean squared error compare with recursive least square.
引用
收藏
页码：388 / +
页数：2
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