Impact of node dynamics parameters on topology identification of complex dynamical networks
首发时间:2013-03-25
Abstract:This paper aims at investigating the topology identification problem of complex dynamicalnetworks with varying node dynamics parameters and fixed inner coupling matrices.In particular, by employing the unified chaotic system as node dynamics, this work furtherexplores the influence of continuously changing node dynamics parameters on topology identificationof complex dynamical networks with different coupling strengths. Results showthat for sufficiently small or large coupling strengths, the performance of topology identificationis not affected by the change of node parameters. Specifically, for enough smallcoupling strengths, the topological structure can be completely identified regardless of thechange of node parameters, while for sufficiently large coupling strengths, none of the connectivity(presence and absence of connections) can be successfully identified. Furthermore,for some certain coupling strengths, with the increase of node dynamics parameters, thetopology identification varies from completely unidentifiable, to partially identifiable, thento completely identifiable. Therefore, the synchronization-based topology identification dependson node dynamics. Even for the same node dynamical model, different parameterscan have a significant impact on identification results. Furthermore, for networks consistingof chaotic oscillators as node dynamics, small coupling strengths are conducive to topologyidentification. A broader conclusion is that projective synchronization, rather than just completesynchronization, is an obstacle to the network topology identification. The findings inthis paper will add to our understanding of conditions for identifying topologies of complex networks.
keywords: Complex network Topology identification Synchronization Node dynamics parameter Chaoticoscillator
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节点动力学参数影响复杂网络结构识别
摘要:本文以统一混沌系统为节点动力学,研究了两种固定内联矩阵和不同耦合强度下,动力学参数的改变对复杂网络结构识别的影响。研究结果表明,在耦合强度足够小或足够大的情况下,节点动力学参数的变动不影响网络拓扑识别的效果。足够小的耦合强度可以使得网络拓扑结构完全识别,而足够大的耦合强度容易使得拓扑识别失败,完全不能识别。而对于某些耦合强度,随着节点动力学参数的增大,网络拓扑结构出现了完全不能识别到部分可识别再到完全识别的过程。这些结果表明:(1)对于混沌振子为节点动力学的网络,小的耦合强度有利于拓扑识别;(2)基于自适应同步技术的网络结构识别依赖于节点动力学,即使同一动力学不同的参数对识别效率也会产生很大的影响;(3)完全内同步阻碍结构识别,更一般的,投影同步也会阻碍网络拓扑识别。这些结果加深了我们对结构识别条件的深入理解和探索。
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