Optimization of fuzzy model using genetic algorithm for process control application
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...
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| Format: | Article |
| Language: | English |
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Elsevier
2011
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| Online Access: | http://psasir.upm.edu.my/id/eprint/23112/ http://psasir.upm.edu.my/id/eprint/23112/1/Optimization%20of%20fuzzy%20model%20using%20genetic%20algorithm%20for%20process%20control%20application.pdf |
| _version_ | 1848844666555858944 |
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| author | Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki Ibrahim, Mohd Faisal |
| author_facet | Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki Ibrahim, Mohd Faisal |
| author_sort | Yusof, Rubiyah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T08:34:33Z |
| format | Article |
| id | upm-23112 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T08:34:33Z |
| publishDate | 2011 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-231122015-12-07T02:30:08Z http://psasir.upm.edu.my/id/eprint/23112/ Optimization of fuzzy model using genetic algorithm for process control application Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki Ibrahim, Mohd Faisal 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. Elsevier 2011-09 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23112/1/Optimization%20of%20fuzzy%20model%20using%20genetic%20algorithm%20for%20process%20control%20application.pdf 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 10.1016/j.jfranklin.2010.10.004 |
| spellingShingle | Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki Ibrahim, Mohd Faisal Optimization of fuzzy model using genetic algorithm for process control application |
| title | Optimization of fuzzy model using genetic algorithm for process control application |
| title_full | Optimization of fuzzy model using genetic algorithm for process control application |
| title_fullStr | Optimization of fuzzy model using genetic algorithm for process control application |
| title_full_unstemmed | Optimization of fuzzy model using genetic algorithm for process control application |
| title_short | Optimization of fuzzy model using genetic algorithm for process control application |
| title_sort | optimization of fuzzy model using genetic algorithm for process control application |
| url | http://psasir.upm.edu.my/id/eprint/23112/ http://psasir.upm.edu.my/id/eprint/23112/ http://psasir.upm.edu.my/id/eprint/23112/1/Optimization%20of%20fuzzy%20model%20using%20genetic%20algorithm%20for%20process%20control%20application.pdf |