在复杂的毕达哥拉斯模糊软矩阵下的新型相似度,以及它们在决策问题中的应用
Muhammad Zeeshan1, Madad Khan2, Ramsha Shafqat3
1Department of Mathematics, The University of Agriculture, Dera Ismail Khan, Pakistan.
Scientific reports
|July 25, 2024
概括
本研究介绍了复杂的毕达哥拉斯模糊软矩阵 (CPFSMs),用于在决策和模式识别中增强不确定性建模. CPFSM提供了一个强大的框架来分析复杂的数据,提高了灵活性和准确性.
科学领域:
- 数学 数学 是一个数学.
- 计算机科学 计算机科学
- 决策科学 决策科学 决策科学
背景情况:
- 复杂的模糊软矩阵对于决策,模式识别,信号和图像处理至关重要.
- 现有的模型可能缺乏复杂的不确定性建模所需的灵活性和准确性.
研究的目的:
- 为高级不确定性建模引入复杂的毕达哥拉斯模糊软矩阵 (CPFSMs).
- 开发新的距离指标和使用CPFSMs的决策技术.
主要方法:
- 定义了复杂的毕达哥拉斯模糊软矩阵的新概念.
- 建立了CPFSM的基本集合理论操作和原则.
- 开发了新的CPFSMs之间的距离指标.
主要成果:
- 在CPFSM框架内引入了CPFS决策技术.
- 通过数值示例和比较分析证明了CPFSM的有效性.
- CPFSMs集成了毕达哥拉斯模糊集,软矩阵和复杂数,以进行强大的建模.
结论:
- CPFSM提供了一个灵活而准确的框架来建模复杂的不确定性.
- 建议的决策技术和距离指标增强了分析能力.
- CPFSM适用于复杂的决策和不确定的数据分析.
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