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 Dierential 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 eectiveness of the proposed method.
کلیدواژه‌های فارسی مقاله
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 Dierential 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 eectiveness of the proposed method.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله مجتبی افتخاری |
faculty of islamic azad university, sirjan branch, ,sirjan, ker- man, iran
سازمان اصلی تایید شده: دانشگاه آزاد اسلامی علوم و تحقیقات (Islamic azad university science and research branch)

مهدی افتخاری |
department of computer engineering, school of engineering, shahid bahonar university of kerman, kerman, iran
سازمان اصلی تایید شده: دانشگاه شهید باهنر (Shahid bahonar university)

مریم مجیدی |
department of computer engineering, school of engineering, shahid bahonar university of kerman, kerman, iran
سازمان اصلی تایید شده: دانشگاه شهید باهنر (Shahid bahonar university)

حسین نظام آبادی پور | nezamabadi pour
department of electrical engineering, school of engi- neering, shahid bahonar university of kerman, kerman, iran
سازمان اصلی تایید شده: دانشگاه شهید باهنر (Shahid bahonar university)

نشانی اینترنتی http://ijfs.usb.ac.ir/article_226_cc28b7c778fbedd749288307752c17c8.pdf
فایل مقاله اشکال در دسترسی به فایل - ./files/site1/rds_journals/448/article-448-131911.pdf
کد مقاله (doi)
زبان مقاله منتشر شده fa
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات