一种新的不完整的犹不决的模糊信息补充和集群方法,用于大规模的集团决策
Jingdong Wang1, Wenhui Wang1, Fanqi Meng1,2
1School of Computer Science, Northeast Electric Power University, Jilin, Jilin, China.
PeerJ. Computer science
|January 25, 2024
概括
本研究引入了一种新的集群方法,用于大规模的集团决策 (LSGDM),有效地处理不完整的信息. 该方法优化了集群中心,并通过结合信任网络和新型公式来提高决策准确性.
科学领域:
- 决策科学 决策科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 大规模集团决策 (LSGDM) 面临的挑战是决策者提供的信息不完整或模两可.
- 传统的集群方法在处理这些数据方面存在困难,导致复杂的计算和不准确的分配.
- 现有的方法往往无法有效处理信任动态和信息传播.
研究的目的:
- 为LSGDM开发一种新的集群方法,解决不完整的犹不决的模糊信息.
- 在复杂的决策场景中提高聚类的准确性和效率.
- 整合信任传播机制,优化聚类中心的选择.
主要方法:
- 提出了一种新的方法来补充不完整的犹不决的模糊信息,考虑决策者和信任邻居的数据.
- 构建全球和本地信任网络,结合信任退化和不信任抑制.
- 开发了使用相对标准偏差理论和密度峰值用于集群中心选择的改进的距离函数.
主要成果:
- 拟议的方法有效补充不完整的犹不决的模糊信息,增强决策数据.
- 新型集群方法优化了集群中心的选择,减少了复杂性和扩展问题.
- 实验结果证明了LSGDM中提出的方法的有效性和可靠性.
结论:
- 开发的方法为处理LSGDM中不完整信息提供了一个强大的解决方案.
- 信任网络和先进的集群技术的整合大大改善了决策过程.
- 这项研究为提高大规模集团决策的效率和准确性提供了有价值的工具.
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