一个新的优化模糊神经网络,用于在k连接的移动临时网络中增强拓控制
Shyam Sundar Agrawal1, Rakesh Rathi2, Shahbaz Ahmed Siddiqui3
1Department of Computer Engineering, Govt. Mahila Engineering College, Ajmer, Rajasthan, India.
Scientific reports
|January 31, 2026
概括
本研究介绍了一种优化的模糊神经网络 (OFNN),用于在移动特设网络 (MANET) 中进行强大的容错拓控制. 新方案提高了网络可靠性和在动态环境中的性能.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 移动特设网络 (MANET) 是动态的,缺乏基础设施的网络,容易因拓变化,链接变化和节点故障而受到干扰.
- 在MANET中保持可靠的通信是具有挑战性的,因为它们固有的不稳定性和易受故障的影响.
- 耐故障拓控制对于确保MANET中有效和安全的连接至关重要.
研究的目的:
- 为 MANET 开发一个高效的耐故障拓控制方案.
- 在动态和不可预测的条件下提高MANET的可靠性和整体性能.
- 引入一个新的优化模糊神经网络 (OFNN),集成先进的优化算法.
主要方法:
- 提出了一种改进的子优化算法,用于战略集群头 (CH) 选择,以优化集群效率.
- 利用优化模糊神经网络 (OFNN) 使用CH输入参数预测路径可靠性:邻近节点距离 (NND),路径稳定性 (PS) 和链接到期时间 (LET).
- 使用Osprey优化算法增强了OFNN性能和网络故障耐受性.
主要成果:
- 该OFNN方案成功地根据关键网络参数预测路径可靠性.
- 通过选择具有最大计算神经元值的路径来优化数据传输,确保高路径可靠性.
- 集成Osprey优化算法进一步完善了网络的故障耐受性和整体效率.
结论:
- 拟议的OFNN方案为MANET中的容错拓控制提供了一个有效的解决方案.
- 优化的集群和路径选择机制显著提高了网络的可靠性和性能.
- 这项研究有助于在动态移动网络中实现更具弹性和更有效的通信.
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