一个对中性粒度集的参数相似度测量及其在能源生产中的应用
Peide Liu1,2, Muhammad Azeem3, Mehwish Sarfraz4
1School of Business Administration, Shandong Women's University, Shandong, Jinan, 250300, China.
Heliyon
|October 22, 2024
概括
本研究引入了一个新的参数相似度衡量单值中性质集 (SVNSs),以解决现有方法的缺点. 拟议的措施有效地解决了多属性决策 (MADM) 问题,优于其他基于SVNS的方法.
科学领域:
- 信息理论是信息理论.
- 模糊的集合理论 模糊的集合理论
- 中性学集合论的中性学集合论.
背景情况:
- 单值中性学集合 (SVNSs) 扩展了模糊集 (FSs) 和直觉模糊集 (IFSs) 来管理模糊数据.
- 对于SVNSs,现有的和相似度有缺点,导致某些应用中的非理性结果.
- 目前的相似度衡量通常依赖于距离,由于复杂的价值运算,这可能产生无意义的结果.
研究的目的:
- 为 SVNSs 提出一种新的参数相似度测量方法,以克服现有方法的局限性.
- 通过参数调整引入一种灵活的相似度衡,可适应不同的决策风格.
- 证明拟议措施在解决多属性决策 (MADM) 问题的有效性.
主要方法:
- 开发了三种可调节参数的SVNS参数相似度测量.
- 从数学角度分析了现有的相似度.
- 使用数学模型验证了拟议的措施,并将其应用于MADM问题.
主要成果:
- 拟议的参数相似度措施有效地处理现有措施中遇到的非理性和无意义的情况.
- 数学分析和建模证实了新测量的有效性和成功.
- 拟议的相似度指标在MADM环境中表现优于其他基于SVNS的相似度指标.
结论:
- 新的参数相似度测量为SVNS数据分析提供了一个强大的和可适应的工具.
- 拟议的措施为解决涉及SVNSs的MADM问题提供了对现有方法的优越方法.
- 可调节的参数允许定制决策,提高措施的适用性.
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