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SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 6، دوره 9، شماره 1، اردیبهشت 2012، صفحه 61-77 اصل مقاله (620.11 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2012.226 | ||
| نویسندگان | ||
| Mojtaba Eftekhari1؛ Mahdi Eftekhari* 2؛ Maryam Majidi3؛ Hossein Nezamabadi pour4 | ||
| 1Faculty of Islamic Azad University, Sirjan branch, ,Sirjan, Ker- man, Iran | ||
| 2Department of Computer Engineering, School of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| 3Department of Computer Engineering, School of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| 4Department of Electrical Engineering, School of Engi- neering, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| چکیده | ||
| In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identi cation, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level interpretability requirements of fuzzy models is especially a complicated task in case of modeling nonlinear MIMO systems. Due to these multiple and conicting objectives, MOGA is applied to yield a set of candidates as compact, transparent and valid fuzzy models. Also, MOGA is combined with a powerful search algorithm namely Di erential Evolution (DE). In the proposed algorithm, MOGA performs the task of membership function tuning as well as rule base identi cation simultaneously while DE is utilized only for linear parameter identi cation. Practical applicability of the proposed algorithm is examined by two nonlinear system modeling prob- lems used in the literature. The results obtained show the e ectiveness of the proposed method. | ||
| کلیدواژهها | ||
| Multi-objective؛ Evolutionary؛ Fuzzy identi cation؛ Compact؛ Inter- pretability | ||
| مراجع | ||
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