通过机器学习技术对某些图形结构进行模糊和清晰的计算分析
Zeeshan Saleem Mufti1, Hadeel AlQadi2, Ali Tabraiz3
1Department of Mathematics and Statistics, The University of Lahore, Lahore Campus, Lahore, Pakistan.
这项研究整合了模糊图形理论和拓指数来分析梯子和网格图形. 知道清晰的图形拓指数可以准确预测模糊的图形值,增强网络分析.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络理论 网络理论
背景情况:
- 模糊图形处理不确定性,扩展复杂网络的传统图形理论.
- 拓索引量化图形结构,对于网络分析和决策至关重要.
研究的目的:
- 将模糊的拓索引应用于梯子和网格图.
- 探索清晰和模糊图形拓索引之间的相关性.
- 利用机器学习来分析这些图形结构.
主要方法:
- 使用模糊图形理论分析梯子和网格图形.
- 计算传统的 (例如,兰迪奇) 和模糊的拓指数 (例如,模糊的萨格勒布指数).
- 机器学习技术和统计分析的应用.
主要成果:
- 在清晰和模糊的梯子图和清晰和模糊的网格图之间发现了强烈的相关性.
- 清晰图形的拓索引可以准确地预测模糊图形的拓索引.
- 机器学习分析提供了一种节省时间和精确的评估方法.
结论:
- 模糊的拓索引为不确定网络环境中的决策提供了强大的框架.
- 基于清晰值的模糊拓指数的预测模型提高了网络可靠性和路线优化.
- 这种创新方法集成了模糊图形理论,拓索引和机器学习,用于高级网络评估.
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