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

Citation

Abdul Rahman, Ribhan Zafira and Yusof, Rubiyah and Khalid, Marzuki (2007) Recursive least square and fuzzy modelling using genetic algorithm for process control application. In: 2007 First Asia International Conference on Modelling & Simulation (AMS 2007), 27-30 Mar. 2007, Phuket, Thailand. (pp. 388-393).

Abstract

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.

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Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Faculty of Engineering
DOI Number:	https://doi.org/10.1109/AMS.2007.83
Publisher:	IEEE
Keywords:	Recursive least square; Process control; Nonlinear process control; Fuzzy modelling
Depositing User:	Nabilah Mustapa
Date Deposited:	04 Aug 2016 08:12
Last Modified:	04 Aug 2016 08:12
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/AMS.2007.83
URI:	http://psasir.upm.edu.my/id/eprint/48267
Statistic Details:	View Download Statistic

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