Optimization of fuzzy model using genetic algorithm for process control application
A technique for the modeling of nonlinear controlprocesses using fuzzy modeling approach based on the Takagi–Sugeno fuzzymodel with a combination of geneticalgorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent par...
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| Format: | Article |
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Elsevier B.V.
2011
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| _version_ | 1796856215618191360 |
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| author | Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki Ibrahim, Mohd. Faizal |
| author_facet | Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki Ibrahim, Mohd. Faizal |
| author_sort | Yusof, Rubiyah |
| collection | ePrints |
| description | A technique for the modeling of nonlinear controlprocesses using fuzzy modeling approach based on the Takagi–Sugeno fuzzymodel with a combination of geneticalgorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzymodel. For the antecedent fuzzy parameters, geneticalgorithm 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 processcontrol 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 | 2024-03-05T18:40:10Z |
| format | Article |
| id | utm.eprints-26542 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:40:10Z |
| publishDate | 2011 |
| publisher | Elsevier B.V. |
| record_format | dspace |
| spelling | utm.eprints-265422018-10-31T12:28:09Z http://eprints.utm.my/26542/ Optimization of fuzzy model using genetic algorithm for process control application Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki Ibrahim, Mohd. Faizal TK Electrical engineering. Electronics Nuclear engineering A technique for the modeling of nonlinear controlprocesses using fuzzy modeling approach based on the Takagi–Sugeno fuzzymodel with a combination of geneticalgorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzymodel. For the antecedent fuzzy parameters, geneticalgorithm 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 processcontrol 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 B.V. 2011 Article PeerReviewed Yusof, Rubiyah and Abdul Rahman, Ribhan Zafira and Khalid, Marzuki and Ibrahim, Mohd. Faizal (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 http://dx.doi.org/10.1016/j.jfranklin.2010.10.004 DOI:10.1016/j.jfranklin.2010.10.004 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Yusof, Rubiyah Abdul Rahman, Ribhan Zafira Khalid, Marzuki Ibrahim, Mohd. Faizal 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 |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| work_keys_str_mv | AT yusofrubiyah optimizationoffuzzymodelusinggeneticalgorithmforprocesscontrolapplication AT abdulrahmanribhanzafira optimizationoffuzzymodelusinggeneticalgorithmforprocesscontrolapplication AT khalidmarzuki optimizationoffuzzymodelusinggeneticalgorithmforprocesscontrolapplication AT ibrahimmohdfaizal optimizationoffuzzymodelusinggeneticalgorithmforprocesscontrolapplication |