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A New Ordered Weighted Averaging Operator to Obtain the Associated Weights Based on the Principle of Least Mean Square Errors
Author(s) -
Bai Chengzu,
Zhang Ren,
Song Chenyang,
Wu Yaning
Publication year - 2017
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21838
Subject(s) - operator (biology) , mathematics , weighted arithmetic mean , measure (data warehouse) , preference , reliability (semiconductor) , function (biology) , mean squared error , parametric statistics , computer science , mathematical optimization , fuzzy logic , algorithm , artificial intelligence , data mining , statistics , biochemistry , chemistry , power (physics) , physics , repressor , quantum mechanics , evolutionary biology , biology , transcription factor , gene
Determining OWA (ordered weighted averaging) weights has received more and more attention since the appearance of the OWA operator. Based on the principle of least mean squared errors, a new parametric OWA operator is proposed to obtain its associated weights. In coordination with fuzzy inference and a few of judgments on weights provided by decision makers (DMs), the new operator is carefully designed to avoid some problems of the existing ones, such as uncertainty in determining an objective function and the measure of orness , etc. Some properties of the problem are discussed to guarantee reliability in theory. A real‐life problem and two simulation experiments are performed to investigate its efficiency. All results show that the proposed operator can be a useful tool to express DMs’ preference information flexibly and objectively.