کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947704 | 1439588 | 2017 | 26 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An enhanced fuzzy min-max neural network with ant colony optimization based-rule-extractor for decision making
ترجمه فارسی عنوان
یک شبکه عصبی مینیممکس حداکثر فازی با حکومت بردار بهینه سازی کلنی برای تصمیم گیری
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This paper proposes an enhanced fuzzy min-max neural (EFMN) network model with ant colony optimization based-rule-extractor for grouping of the data-patterns and decision making by rule-list. There are many methods to extract the rules which are having less accuracy and with less performance. The earlier methods have drawbacks like they have not maintained the rule accuracy in terms of consistency. The proposed method has improved the accuracy, consistency and performance against existing methods. The number of rules obtained from this system is less in count and having higher rank. One of the strength of this method is that the rules obtained from the system are comprehensible, as they are in rule list format. This rule list is useful in decision making problem.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 239, 24 May 2017, Pages 204-213
Journal: Neurocomputing - Volume 239, 24 May 2017, Pages 204-213
نویسندگان
Preetee M. Sonule, Balaji S. Shetty,