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Lanchester equation for cognitive domain using hesitant fuzzy linguistic terms sets

Journal of Systems Engineering and Electronics 2022年第3期33卷 674-682页

作者： HAN Qi LI Weimin XU Qiling ZHAO Minrui HUO Runze ZHANG Tao Air Defense and Antimissile School Air Force Engineering UniversityXi’an 710051China School of Humanities Economics and LawNorthwestern Polytechnical UniversityXi’an 710051China

Intelligent wars can take place not only in the physical domain and information domain but also in the cognitive domain.The cognitive domain will become the key domain to win in the future intelligent war.A Lanchester equation considering cognitive domain is proposed to fit the development tendency intelligent wars in this paper.One party is considered to obtain the exponential enhancement advantage on combat forces in combat if it can gain an advantage in the cognitive domain over the other party according to the systemic advantage function.The operational effectiveness of the cognitive domain in war is considered to consist of a series of indicators.hesitant fuzzy sets and linguistic term sets are powerful tools when evaluating indicators,hence the indicators are scored by experts using hesitant fuzzy linguistic terms sets here.A unique hesitant fuzzy hybrid arithmetical averaging operator is used to aggregate the evaluation.

关键词： Lanchester equation cognitive domain hesitant fuzzy set hesitant fuzzy hybrid arithmetical averaging operator

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Hierarchical hesitant fuzzy K-means clustering algorithm

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Applied Mathematics(A Journal of Chinese Universities) 2014年第1期29卷 1-17页

作者： CHEN Na XU Ze-shui XIA Mei-mei School of Economics and Management Southeast University School of Applied Mathematics Nanjing University of Finance and Economics Business School Sichuan University School of Economics and Management Tsinghua University

Due to the limitation and hesitation in one＇s knowledge, the membership degree of an element to a given set usually has a few different values, in which the conventional fuzzy sets are invalid. hesitant fuzzy sets are a powerful tool to treat this case. The present paper focuses on investigating the clustering technique for hesitant fuzzy sets based on the K-means clustering algorithm which takes the results of hierarchical clustering as the initial clusters. Finally, two examples demonstrate the validity of our algorithm.

关键词： 90B50 68T10 62H30 hesitant fuzzy set hierarchical clustering K-means clustering intuitionisitc fuzzy set

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