Structure identification of uncertain general complex dynamical networks with time delay

Topology detection for output-coupling weighted complex dynamical networks with two types of time delays is investigated in this paper. Different from existing literatures, coupling delay and transmission delay are simultaneously taken into account in the output-coupling network. Based on the idea of the state observer, we build the drive-response system and apply LaSalle's invariance principle to the error dynamical system of the drive-response system. Several convergent criteria are deduced in the form of algebraic inequalities. Some numerical simulations for the complex dynamical network, with node dynamics being chaotic, are given to verify the effectiveness of the proposed scheme.

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“…. , }, { ( − 1 )} =1 is linearly independent of the orbit { ( − 1 )} =1 of the synchronization manifold [31,32].…”

Section: Assumption 2 (A2) Suppose That There Exists a Nonnegative Cmentioning

confidence: 99%

“…It characterizes the property of the node dynamics. In [31][32][33], all the coupling terms must be linearly independent of the synchronization manifold between the drive and response systems. (A3) is a necessary condition for the purpose of detecting the network topology.…”

Section: Lemma 4 (L4)mentioning

confidence: 99%

Topology Detection for Output‐Coupling Weighted Complex Dynamical Networks with Coupling and Transmission Delays

Wang

Jiang

Cheng

et al. 2017

Mathematical Problems in Engineering

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“…Remark 3.3 Unlike many previous schemes [7][8][9][10]40], the constructed auxiliary network can be consisting of any kind of dynamical nodes other than nodes with identical dynamics as their counterparts in the drive layer. Therefore, if the considered network is composed of nodes carrying very complicated node dynamics or high node dimensions, one can design a response layer using nodes with much simpler dynamics.…”

Section: Remark 32 It Is Clear That the Coupling Configuration Matrixmentioning

confidence: 99%

“…The diffusion operator L defined in (3) onto the function V along the trajectories of the error dynamics (9) gives…”

Section: Proof Consider the Following Lyapunov Functionmentioning

confidence: 99%

“…In the past few years, a great variety of methods have been developed for inferring network topologies, using technologies such as the synchronization-based method [7][8][9][10][11], compressive sensing [12], Bayesian estimation [13], recurrence [14,15], Granger causality test [16,17], node knockout [18], echo state mechanism [19], and so on. Among these methods, the one based on synchronization in which some adaptive controllers are designed so that an auxiliary response network can synchronize with the uncertain network and the topological parameters can be estimated simultaneously, has been paid wide attention to.…”

Section: Introductionmentioning

confidence: 99%

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Inferring topologies via driving-based generalized synchronization of two-layer networks

Wang

Feng

et al. 2016

J. Stat. Mech.

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Abstract. The interaction topology among the constituents of a complex network plays a crucial role in the network's evolutionary mechanisms and functional behaviors. However, some network topologies are usually unknown or uncertain. Meanwhile, coupling delay are ubiquitous in various man-made and natural networks. Hence, it is necessary to gain knowledge of the whole or partial topology of a complex dynamical network by taking into consideration communication delay. In this paper, topology identification of complex dynamical networks is investigated via generalized synchronization of a two-layer network. Particularly, based on the LaSalle-type invariance principle of stochastic differential delay equations, an adaptive control technique is proposed by constructing an auxiliary layer and designing proper control input and updating laws so that the unknown topology can be recovered upon successful generalized synchronization. Numerical simulations are provided to illustrate the effectiveness of the proposed method. The technique provides a certain theoretical basis for topology inference of complex networks. In particular, when the considered network is composed of systems with high-dimension or complicated dynamics, a simpler response layer can be constructed, which is conducive to circuit design. Moreover, it is practical to take into consideration perturbations caused by control input. Finally, the method is applicable to infer topology of a subnetwork embedded within a complex system and locate hidden sources. We hope the results can provide basic insight into further research endeavors on understanding practical and economical topology inference of networks.

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Adaptive control and signal processing literature survey (No. 15)

2009

Adaptive Control & Signal

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In order to keep readers up to date, the journal will contain a list of recently published journal articles relevant to the fields of robust and nonlinear control six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Leonardo Giovanini at lgiovanini@fich.unl.edu.ar. 2009; 57(9):3488-3497.Karafyllis I, Kravaris C. From continuous-time design to sampled-data design of observers. IEEE Transactions on

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Structure identification of uncertain general complex dynamical networks with time delay

Cited by 250 publications

References 34 publications

Topology Detection for Output‐Coupling Weighted Complex Dynamical Networks with Coupling and Transmission Delays

Topology Detection for Output‐Coupling Weighted Complex Dynamical Networks with Coupling and Transmission Delays

Inferring topologies via driving-based generalized synchronization of two-layer networks

Adaptive control and signal processing literature survey (No. 15)

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