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An argument‐dependent approach to determining OWA operator weights based on the rule of maximum entropy
Author(s) -
Wu Jian,
Liang Changyong,
Huang Yongqing
Publication year - 2007
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.20201
Subject(s) - operator (biology) , entropy (arrow of time) , principle of maximum entropy , mathematics , computer science , argument (complex analysis) , basis (linear algebra) , algorithm , mathematical optimization , artificial intelligence , biochemistry , chemistry , physics , repressor , quantum mechanics , transcription factor , gene , geometry
The methods for determining OWA operator weights have aroused wide attention. We first review the main existing methods for determining OWA operator weights. We next introduce the principle of maximum entropy for setting up probability distributions on the basis of partial knowledge and prove that Xu's normal distribution‐based method obeys the principle of maximum entropy. Finally, we propose an argument‐dependent approach based on normal distribution, which assigns very low weights to these “false” or “biased” opinions and can relieve the influence of the unfair arguments. A numerical example is provided to illustrate the application of the proposed approach. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 209–221, 2007.