在通信网络中提取知识的混合机器学习技术和基于先进决策的复杂q-Rung orthopair模糊框架
Hushuang Zeng1, Yonghua Zhang2
1Digital and Intelligent Operations Center, Guangxi Power Grid Co., Ltd, Nanning, 530022, China.
Scientific reports
|August 21, 2025
概括
混合机器学习和复杂的Q-Rung整形模糊集增强了通信网络的智能. 这些方法改善了数据分析,以支持数据驱动应用的弹性,高效的网络.
科学领域:
- 计算机科学
- 人工智能
- 信息理论
背景情况:
- 现代通信网络产生了庞大而复杂的数据.
- 现有的方法难以实现高维度和动态网络行为.
- 需要先进的技术来提高网络智能和效率.
研究的目的:
- 介绍和评估复杂的q-rung整形对模糊集 (Cq-ROFS) 以获取知识.
- 开发用于通信网络分析的混合机器学习技术.
- 评估新的决策方法的效率和可行性.
主要方法:
- 使用Cq-ROFS扩展直觉和q-rung整形模糊模型.
- 使用Sugeno-Weber t-规范/t-规范修改的加权平均和几何运算符.
- 应用多标准决策 (MCDM) 和敏感性分析的WASPAS方法.
主要成果:
- 已证明的Cq-ROFS通过振幅和相位术语有效地捕获复杂的网络信息.
- 通过先进通信技术的实验案例研究验证了混合方法.
- 通过比较分析展示了与现有方法相比的灵活性和优势.
结论:
- 使用Cq-ROFS的混合机器学习为智能通信网络提供了强大的框架.
- 开发的MCDM方法提供了一个可靠的决策分析系统.
- 拟议的方法提高了数据驱动应用的网络弹性和效率.
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