摘要： 为解决复杂环境下网络拓扑中节点选取难的问题,提出一种基于直觉模糊集的随机最小支撑树选取方法。通过剖析网络拓扑结构中信息的随机、模糊等不确定性,将随机模糊变量扩展为随机直觉模糊变量,对拓扑结构中的边权进行度量。将网络拓扑结构中的最小支撑树问题转化为随机直觉模糊支撑树问题,利用截集去模糊化和Prim算法求取最优解,以优化网络的拓扑结构。通过网络实验验证了所提方法的有效性和合理性。

关键词: 复杂环境, 拓扑结构, 直觉模糊集, 随机模糊变量, 随机直觉模糊变量, 最小支撑树

Abstract: To solve the difficulty of selecting nodes for network topology in complex environment,a selection method of random minimum spanning tree based on intuitionistic fuzzy sets is proposed.Through analyzing the uncertainty,like randomness and fuzziness,information in the network topology design,random fuzzy variables are expanded for random intuitionistic fuzzy variables,and edge weights in the topological structure are measured.The minimum spanning tree problem in network topology is transformed into random intuitionistic fuzzy spanning tree problem.Cut sets are used to remove fuzziness,and Prim algorithm to obtain the optimal solution,which will optimize the network topology.The effectiveness and rationality of the method is verified through a network experiment.

Key words: complex environment, topology structure, intuitionistic fuzzy set, random fuzzy variable, random intuitionistic fuzzy variable, minimum spanning tree

中图分类号: 

• TP393