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Identifying topologies and system parameters of uncertain time-varying delayed complex networks

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Abstract

Node dynamics and network topologies play vital roles in determining the network features and network dynamical behaviors. Thus it is of great theoretical significance and practical value to recover the topology structures and system parameters of uncertain complex networks with available information. This paper presents an adaptive anticipatory synchronization-based approach to identify the unknown system parameters and network topological structures of uncertain time-varying delayed complex networks in the presence of noise. Moreover, during the identification process, our proposed scheme guarantees anticipatory synchronization between the uncertain drive and constructed auxiliary response network simultaneously. Particularly, our method can be extended to several special cases. Furthermore, numerical simulations are provided to verify the effectiveness and applicability of our method for reconstructing network topologies and node parameters. We hope our method can provide basic insight into future research on addressing reconstruction issues of uncertain realistic and large-scale complex networks.

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Authors and Affiliations

  1. LSC, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Xiong Wang, HaiBo Gu, QianYao Wang & JinHu Lü

  2. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China

    Xiong Wang, HaiBo Gu & QianYao Wang

  3. School of Automation Science and Electrical Engineering, State Key Laboratory of Software Development Environment, and Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China

    JinHu Lü

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  1. Xiong Wang
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  2. HaiBo Gu
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  3. QianYao Wang
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  4. JinHu Lü
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Correspondence to JinHu Lü.

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Wang, X., Gu, H., Wang, Q. et al. Identifying topologies and system parameters of uncertain time-varying delayed complex networks. Sci. China Technol. Sci. 62, 94–105 (2019). https://doi.org/10.1007/s11431-018-9287-0

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  • DOI: https://doi.org/10.1007/s11431-018-9287-0

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