Topology identification of multi‐weighted complex networks based on adaptive synchronization: A graph‐theoretic approach

Abstract

This paper aims to identify the topological structures of multi-weighted complex networks (MWCN) with and without time delay based on adaptive synchronization. First, by using the concept of drive-response synchronization, one takes the MWCN as a drive system and introduces the adaptive controller and white noise in the response system to get stochastic MWCN. Then, drive system and response system achieve synchronization by combining graph theory, Lyapunov stability theory, and adaptive control technique. At the same time, the uncertain multiple topological structures of drive system can be identified by response system based on adaptive synchronization. Furthermore, topological structures of MWCN with time delay are also successfully identified. Finally, numerical simulations for the identification of coupled chaotic systems are provided to verify the effectiveness of theoretical results.

Graph‐theoretic approach to finite‐time synchronization and topology identification of delayed multi‐group models with stochastic perturbation and multiple dispersal

“…Remark In the previous researches, some results were obtained about topology identification of many complex networks by the graph‐theoretic approach [43, 44]. However, they did not consider topology identification in finite time.…”

Structure Identification of Fractional-Order Dynamical Network with Different Orders

“…Remark 2. Recently, many valuable results about topology identification of integer-order networks have been obtaining by various methods, such as the graph-theoretic approach [8], compressive sensing [9], mixed integer linear program (MILP) method [10], and so on. Compared with these results, we consider the structure identification problem of a fractional-order network.…”

“…Since the DNA interactions have a great effect on cellular processes, how to effectively identify them is an interesting and important issue and deserves deep study. Thus far, researchers have performed much research on the structure identification issue of dynamical networks [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. In [15], Waarde et al addressed the problem of identifying the graph structure of a dynamical network using measured input/output data.…”

Identifying Partial Topological Structures of Stochastic Multi-Group Models with Multiple Dispersals via Graph-Theoretic Method

“…Remark 2. Though the adaptive control method can realize topology identification of multi-group models, it needs to add a controller to every group [27,28]. However, in this paper, the model includes a great number of groups.…”