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Iranian Journal of Fuzzy Systems، جلد ۹، شماره ۱، صفحات ۶۱-۷۷
عنوان فارسی SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
چکیده فارسی مقاله 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.
کلیدواژه‌های فارسی مقاله Multi-objective، Evolutionary، Fuzzy identi cation، Compact، Inter- pretability،
عنوان انگلیسی SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
چکیده انگلیسی مقاله 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.
کلیدواژه‌های انگلیسی مقاله Multi-objective, Evolutionary, Fuzzy identi cation, Compact, Inter- pretability
نویسندگان مقاله Mojtaba Eftekhari |
Faculty of Islamic Azad University, Sirjan branch, ,Sirjan, Ker- man, Iran

Mahdi Eftekhari |
Department of Computer Engineering, School of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

Maryam Majidi |
Department of Computer Engineering, School of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran

Hossein Nezamabadi pour |
Department of Electrical Engineering, School of Engi- neering, Shahid Bahonar University of Kerman, Kerman, Iran

نشانی اینترنتی http://ijfs.usb.ac.ir/article_226_cc28b7c778fbedd749288307752c17c8.pdf
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