Optimization of fuzzy model using genetic algorithm for process control application

Citation

Yusof, Rubiyah and Abdul Rahman, Ribhan Zafira and Khalid, Marzuki and Ibrahim, Mohd Faisal (2011) Optimization of fuzzy model using genetic algorithm for process control application. Journal of the Franklin Institute, 348 (7). pp. 1717-1737. ISSN 0016-0032; ESSN: 1879-2693

Abstract

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.

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Additional Metadata

Item Type:	Article
Divisions:	Faculty of Engineering
DOI Number:	https://doi.org/10.1016/j.jfranklin.2010.10.004
Publisher:	Elsevier
Keywords:	Fuzzy model; Genetic algorithm; Recursive least squares; Process control
Depositing User:	Nabilah Mustapa
Date Deposited:	07 Dec 2015 02:30
Last Modified:	07 Dec 2015 02:30
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.jfranklin.2010.10.004
URI:	http://psasir.upm.edu.my/id/eprint/23112
Statistic Details:	View Download Statistic

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