文章基本信息

标题：Topology Identification of Dynamical Networks via Compressive Sensing
本地全文：下载
作者：Sina Jahandari ; Donatello Materassi
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：575-580
DOI：10.1016/j.ifacol.2018.09.220
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, we address the problem of topology identification of causal dynamical networks. Collecting the outputs of the nodes as time series, without any a priori knowledge about the structure of the network, we propose a data-driven algorithm that unveils the topology of the network and identifies the dynamics of connections. We cast the problem as a structured sparse signal recovery based on concepts borrowed from compressive sensing and matching pursuit. When sufficient data is available, the proposed algorithm results in perfect identification of general networks including feedback and self-loops. For noninvasive data, the proposed algorithm outperforms existing techniques. To demonstrate the effectiveness and advantages of the proposed method, we compare the simulation results with those of the Granger causality and other state-of-the-art techniques. As an empirical application, the proposed algorithm is deployed to construct a graphical network describing the interconnections of 30 companies in the Dow Jones stock market index with their prices ranging from January 2012 to June 2017.
关键词：KeywordsCausal dynamical networksCompressive sensingMatching pursuitGranger causalityNoninvasive measurements