Evaluation of Fuzzy Pareto Solution Set by Using Fuzzy Relation Based Clustering Approach For Fuzzy Multi-Response Experiments | Zendy

Özlem Türkşen | Zendy; Ayşen Apaydın | Zendy

Open Access

Evaluation of Fuzzy Pareto Solution Set by Using Fuzzy Relation Based Clustering Approach For Fuzzy Multi-Response Experiments

Author(s) -

Özlem Türkşen,

Ayşen Apaydın

Publication year - 2013

Publication title -

sakarya university journal of science

Language(s) - English

Resource type - Journals

eISSN - 2147-835X

pISSN - 1301-4048

DOI - 10.5505/saufbe.2013.70288

Subject(s) - fuzzy logic , relation (database) , pareto principle , fuzzy set , fuzzy clustering , mathematics , fuzzy classification , computer science , data mining , artificial intelligence , mathematical optimization

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

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