图像通过模糊软外平面图形结构的收缩
Deivanai Jaisankar1, Sujatha Ramalingam2, Gizachew Bayou Zegeye3
1Mathematics School of Science and Humanities, Shiv Nadar University Chennai, Rajiv Gandhi Salai (OMR), Kalavakkam, Chengalpattu, Tamil Nadu, 603110, India.
Scientific reports
|February 17, 2026
概括
本研究介绍了模糊软外平面图 (FSOGs),这是一个新的数学框架,结合模糊集理论和软集与外平面图结构,以建模复杂数据网络中的不确定性.
科学领域:
- 图形理论 图形理论
- 集合理论 集合理论
- 计算数学 计算数学 计算数学
背景情况:
- 模糊集和软集是建立的数学框架,用于管理不确定性和模糊性.
- 外平面图,平面图的一个子集,为复杂的数据网络提供了简化的结构.
- 现有的图形模型很难有效地表示模两可或部分定义的关系.
研究的目的:
- 介绍和定义模糊软外平面图 (FSOGs) 作为清晰外平面图的概括.
- 探索FSOG在处理不确定性的特性和应用.
- 扩展图形理论的概念,以纳入模糊和软集特征.
主要方法:
- 模糊集合理论的集成用于对顶点和边缘的分级成员赋值.
- 软集参数用于上下文依赖图形表示的应用.
- 顶部删除 (VD) 和边缘删除 (ED) 子图的表述,包括最大和最大的FSOG.
- 对模糊软平面图的双重概念的开发.
主要成果:
- FSOG通过结合模糊性和软设置灵活性,有效地模拟模糊的关系.
- 建立了新的子图类型 (VD和ED) 和模糊软平面图的双图概念.
- 理论发现以正式定理和说明性示例来证明.
- 在图像收缩中展示了潜在的应用.
结论:
- FSOG提供了一个强大的数学框架来表示图形结构中的不确定性.
- 开发的概念为分析复杂和模糊的数据网络提供了增强的能力.
- 这项研究为模糊图形理论及其应用领域的进一步研究开辟了道路.
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