The solution set of a multi-response experiment is characterized by Pareto solution set. In this paper, the multiresponse experiment is dealed in a fuzzy framework. The responses and model parameters are considered as triangular fuzzy numbers which indicate the uncertainty of the data set. Fuzzy least square approach and fuzzy modified NSGA-II (FNSGA-II) are used for modeling and optimization, respectively. The obtained fuzzy Pareto solution set is grouped by using fuzzy relational clustering approach. Therefore, it could be easier to choose the alternative solutions to make better decision. A fuzzy response valued real data set is used as an application.
Fuzzy multi-response problem fuzzy modeling fuzzy NSGA-II fuzzy Pareto solution set fuzzy relational clustering
The solution set of a multi-response experiment is characterized by Pareto solution set. In this paper, the multi-response experiment is dealed in a fuzzy framework. The responses and model parameters are considered as triangular fuzzy numbers which indicate the uncertainty of the data set. Fuzzy least square approach and fuzzy modified NSGA-II (FNSGA-II) are used for modeling and optimization, respectively. The obtained fuzzy Pareto solution set is grouped by using fuzzy relational clustering approach. Therefore, it could be easier to choose the alternative solutions to make better decision. A fuzzy response valued real data set is used as an application.
Bulanık çok yanıtlı problem bulanık modelleme bulanık BSGA-II bulanık Pareto çözüm kümesi bulanık ilişkili sınıflandırma
Birincil Dil | Türkçe |
---|---|
Konular | Mühendislik |
Bölüm | Araştırma Makalesi |
Yazarlar | |
Yayımlanma Tarihi | 1 Nisan 2013 |
Gönderilme Tarihi | 20 Kasım 2012 |
Kabul Tarihi | 25 Aralık 2012 |
Yayımlandığı Sayı | Yıl 2013 Cilt: 17 Sayı: 1 |
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