SECURING INTERPRETABILITY OF FUZZY MODELS FOR MODELING NONLINEAR MIMO SYSTEMS USING A HYBRID OF EVOLUTIONARY ALGORITHMS
(ندگان)پدیدآور
Eftekhari, MojtabaEftekhari, MahdiMajidi, MaryamNezamabadi pour, Hosseinنوع مدرک
TextResearch Paper
زبان مدرک
Englishچکیده
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.
کلید واژگان
Multi-objectiveEvolutionary
Fuzzy identi cation
Compact
Inter- pretability
شماره نشریه
1تاریخ نشر
2012-02-011390-11-12
ناشر
University of Sistan and Baluchestanسازمان پدید آورنده
Faculty of Islamic Azad University, Sirjan branch, ,Sirjan, Ker- man, IranDepartment of Computer Engineering, School of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Department of Computer Engineering, School of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Department of Electrical Engineering, School of Engi- neering, Shahid Bahonar University of Kerman, Kerman, Iran
شاپا
1735-06542676-4334