کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947874 | 1439595 | 2017 | 39 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Interactive evolutionary optimization of fuzzy cognitive maps
ترجمه فارسی عنوان
بهینه سازی تکاملی تعاملی نقشه های شناختی فازی
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کلمات کلیدی
00-01، 99-00، نقشه شناختی فازی، بهینه سازی تکاملی تعاملی، دانش کارشناس،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Modeling dynamic systems with Fuzzy Cognitive Maps (FCMs) is characterized by the simplicity of the model representation and its execution. Furthermore, FCMs can easily incorporate human knowledge from the given domain. Despite the many advantages of FCMs, there are some drawbacks, too. The quality of knowledge obtained from the domain experts, and any differences and uncertainties in their opinions, has to be improved by different methods. We propose a new approach for handling incompleteness and natural uncertainty in expert evaluation of the connection matrix of a particular FCM. It is based on partial expert estimations and evolutionary algorithms in the role of an expert-driven optimization and outside of the FCM optimization (adaptation) research area known as Interactive Evolutionary Computing (IEC). In the present paper, a modification of IEC for the purposes of FCM optimization is presented, referred to as the IEO-FCM method, i.e., the Interactive Evolutionary Optimization of Fuzzy Cognitive Maps. Experimental results on two control problems suggest that the IEO-FCM method can improve the quality of an FCM even in situations without any measured data necessary for other known learning algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 232, 5 April 2017, Pages 58-68
Journal: Neurocomputing - Volume 232, 5 April 2017, Pages 58-68
نویسندگان
Karel Mls, Richard Cimler, Ján VaÅ¡Äák, Michal Puheim,