Interaction power Heronian mean aggregation operators for multiple attribute decision making with T-spherical fuzzy information

操作员(生物学) 违反直觉 模糊逻辑 学位(音乐) 数学 功率(物理) 灵敏度(控制系统) 变量(数学) 计算机科学 过程(计算) 数学优化 算法 数据挖掘 人工智能 操作系统 基因 工程类 声学 转录因子 电子工程 量子力学 抑制因子 认识论 物理 哲学 化学 生物化学 数学分析
作者
Haolun Wang,Faming Zhang
出处
期刊:Journal of Intelligent and Fuzzy Systems [IOS Press]
卷期号:42 (6): 5715-5739 被引量:10
标识
DOI:10.3233/jifs-212149
摘要

The interaction operation laws (IOLs) between membership functions can effectively avoid the emergence of counterintuitive situations. The power average (PA) operator can eliminate the negative effect of extremely or improperly assessments on the decision results. The Heronian mean (HM) operator is capable of examining the interrelationship between the two attributes. To synthesize the powers of the IOLs, PA and HM operators in this paper, the PA and HM operators are extended to process T-spherical fuzzy evaluation information perfectly based on the IOLs, and the T-spherical fuzzy interaction power Heronian mean (T-SFIPHM) operator and its weighted form are proposed. We further present some properties of these proposed AOs and discuss several special cases. Moreover, a novel method to T-spherical fuzzy multiple attribute decision making (MADM) problems applying the proposed AO is developed. Lastly, we present a numerical example to validate its feasibility and reasonableness, and the superiority of the developed method is further illustrated by sensitivity analysis of parameters and comparison with existing methods. The results show that proposed AOs not only can capture the interactivity among membership degree (MD), abstinence degree (AD) and non-membership degree (NMD) of T-spherical fuzzy numbers (T-SFNs), bust also ensure the overall balance of variable values in the process of information fusion and realize the interrelationship between attribute variables, so the decision results can be closer to reality and more reliable.

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