Abstract
This paper is concerned with topology identification of stochastic multi-weighted complex networks based on adaptive synchronization. Different from previous work, time-varying delayed coupling is taken into account. By using Lyapunov method, graph theory and LaSalle-type invariance principle for stochastic differential equations, the whole topological structures and partial topological structures are successfully identified under adaptive control and adaptive pinning control respectively. Based on drive-response concept, response network can reach synchronization with drive network. In the end, two numerical examples are provided to demonstrate the effectiveness and correctness of our theoretical results, where typical Lü chaotic system is used to describe the vertex’s dynamical behavior.
Similar content being viewed by others
References
Low CY, Park J, Teoh ABJ (2020) Stacking-based deep neural network: deep analytic network for pattern classification. IEEE Trans Cybern 50:5021–5034
Park BS, Kwon JW, Kim H (2017) Neural network-based output feedback control for reference tracking of underactuated surface vessels. Automatica 77:353–359
Shi K, Wang J, Zhong S, Zhang X, Liu Y, Cheng J (2019) New reliable nonuniform sampling control for uncertain chaotic neural networks under Markov switching topologies. Appl Math Comput 347:169–193
Li X, Yang G (2018) Neural-network-based adaptive decentralized fault-tolerant control for a class of interconnected nonlinear systems. IEEE Trans Neural Netw Learn Syst 29:144–155
Fu J, Wang Q, Wang J (2019) Robust finite-time consensus tracking for second-order multi-agent systems with input saturation under general directed communication graphs. Int J Control 92:1785–1795
Zhang T, Ye D (2020) Distributed secure control against denial-of-service attacks in cyber-physical systems based on K-connected communication topology. IEEE Trans Cybern 50:3094–3103
Molzahn DK, Dorfler F, Sandberg H, Low SH, Chakrabarti S, Baldick R, Lavaei J (2017) A survey of distributed optimization and control algorithms for electric power systems. IEEE Trans Smart Grid 8:2941–2962
Liu R, Meng G, Yang B, Sun C, Chen X (2017) Dislocated time series convolutional neural architecture: an intelligent fault diagnosis approach for electric machine. IEEE Trans Ind Inform 13:1310–1320
Zhao X, Zhou J, Lu J (2019) Pinning synchronization of multiplex delayed networks with stochastic perturbations. IEEE Trans Cybern 49:4262–4270
Wu Y, Zhuang S, Li W (2019) Periodically intermittent discrete observation control for synchronization of the general stochastic complex network. Automatica 110:108591
Kuate PDK, Lai Q, Fotsin H (2019) Dynamics, synchronization and electronic implementations of a new Lorenz-like chaotic system with nonhyperbolic equilibria. Int J Bifurcation Chaos 29:1950197
Kong F, Zhu Q, Sakthivel R, Mohammadzadeh A (2021) Fixed-time synchronization analysis for discontinuous fuzzy inertial neural networks with parameter uncertainties. Neurocomputing 422:295–313
Wang M, Guo J, Li W (2020) Aperiodically intermittent control for stabilization of random coupled systems on networks with Markovian switching. Neurocomputing 373:1–14
Zhang C, Han B (2020) Stability analysis of stochastic delayed complex networks with multi-weights based on Razumikhin technique and graph theory. Physica A 538:122827
Wang B, Zhu Q (2018) Stability analysis of semi-Markov switched stochastic systems. Automatica 94:72–80
Chen J, Li J, Zhang R, Wei C (2019) Distributed fuzzy consensus of uncertain topology structure multi-agent systems with non-identical partially unknown control directions. Appl Math Comput 362:124581
Xu Y, Zhou W, Zhang J, Sun W, Tong D (2017) Topology identification of complex delayed dynamical networks with multiple response systems. Nonlinear Dyn 88:2969–2981
Wang Y, Wu X, Lü J, Lu J, D’Souza RM (2020) Topology identification in two-layer complex dynamical networks. IEEE Trans Netw Sci Eng 7:538–548
Yao X, Xia D, Zhang C (2021) Topology identification of multi-weighted complex networks based on adaptive synchronization: a graph-theoretic approach. Math Methods Appl Sci 44:1570–1584
Xu J, Zhang J, Tang W (2013) Parameters and structure identification of complex delayed networks via pinning control. Trans Inst Meas Control 35:619–624
Zhao H, Li L, Peng H, Xiao J, Yang Y, Zheng M (2017) Finite-time topology identification and stochastic synchronization of complex network with multiple time delays. Neurocomputing 219:39–49
Zhang H, Wang X, Lin X (2017) Topology identification and module-phase synchronization of neural network with time delay. IEEE Trans Syst Man Cybern Syst 47:885–892
Zhao X, Zhou J, Zhu S, Ma C, Lu J (2020) Topology identification of multiplex delayed networks. IEEE Trans Circuits Syst II Express Briefs 67:290–294
Wang L, Zhang J, Sun W (2019) Adaptive outer synchronization and topology identification between two complex dynamical networks with time-varying delay and disturbance. IMA J Math Control Inf 36:946–961
Wu X (2008) Synchronization-based topology identification of weighted general complex dynamical networks with time-varying coupling delay. Physica A 387:997–1008
Shahrampour S, Preciado VM (2015) Topology identification of directed dynamical networks via power spectral analysis. IEEE Trans Autom Control 60:2260–2265
Xu Y, Zhou W, Fang J, Sun W, Pan L (2012) Topology identification and adaptive synchronization of uncertain complex networks with non-derivative and derivative coupling. J Frankl Inst 349:1951–1953
Zhou J, Yu W, Li X, Small M, Lu J (2009) Identifying the topology of a coupled FitzHugh–Nagumo neurobiological network via a pinning mechanism. IEEE Trans Neural Netw 20:1679–1684
Zhu S, Zhou J, Lu J (2018) Identifying partial topology of complex dynamical networks via a pinning mechanism. Chaos 28:043108
Li X, Sun JQ (2018) Signal multiobjective optimization for urban traffic network. IEEE Trans Intell Transp Syst 19:3529–3537
Wu Y, Zhu J, Li X (2020) Intermittent discrete observation control for synchronization of stochastic neural networks. IEEE Trans Cybern 50:2414–2424
Li S, Lv C, Ding X (2020) Synchronization of stochastic hybrid coupled systems with multi-weights and mixed delays via a periodically adaptive intermittent control. Nonlinear Anal Hybrid Syst 35:100819
Wang P, Wang W, Su H, Feng J (2020) Stability of stochastic discrete-time piecewise homogeneous Markov jump systems with time delay and impulsive effects. Nonlinear Anal Hybrid Syst 38:100916
Mao X (2007) Stochastic differential equations and applications, 2nd edn. Horwood Publishing, Chichester
Bernt Ø (2006) Stochastic differential equations. Springer, Berlin
Li MY, Shuai Z (2010) Global-stability problem for coupled systems of differential equations on networks. J Differ Equ 248:1–20
Zhang C, Shi L (2021) Graph-theoretic method on the periodicity of coupled predator–prey systems with infinite delays on a dispersal network. Physica A 561:125255
Liu Y, Li W, Feng J (2018) The stability of stochastic coupled systems with time-varying coupling and general topology structure. IEEE Trans Neural Netw Learn Syst 29:4189–4200
Xu Y, Zhou H, Li W (2020) Stabilisation of stochastic delayed systems with Lévy noise on networks via periodically intermittent control. Int J Control 93:505–518
Zhou H, Li W (2019) Synchronisation of stochastic-coupled intermittent control systems with delays and Lévy noise on networks without strong connectedness. IET Control Theory Appl 13:36–49
Mao X (2002) A note on the LaSalle-type theorems for stochastic differential delay equations. J Math Anal Appl 268:125–142
Mao X (2006) Stochastic differential equations with Markovian switching. Imperial College Press, London
Wang G, Li W, Feng J (2017) Stability analysis of stochastic coupled systems on networks without strong connectedness via hierarchical approach. J Frankl Inst 354:1138–1159
Lu J, Chen G (2002) A new chaotic attractor coined. Int J Bifurcation Chaos 12:659–661
Acknowledgements
The authors really appreciate the anonymous reviewers for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (No.11601445) and the Fundamental Research Funds for the Central Universities, PR China (No. 2682020ZT109).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
The initial conditions \(\phi _{i}\) and \(\psi _{i}\) for drive system (26) and response system (27) are shown as follows.
in which \(\psi _{i2}(s)=-0.3\cos s+10\), \(\psi _{i3}(s)=-0.2\cos s+11\) for \(i=1,2,\ldots ,10\).
Rights and permissions
About this article
Cite this article
Chen, H., Zhang, C., Xu, Q. et al. Graph-Theoretic Method on Topology Identification of Stochastic Multi-weighted Complex Networks with Time-Varying Delayed Coupling Based on Adaptive Synchronization. Neural Process Lett 54, 181–205 (2022). https://doi.org/10.1007/s11063-021-10625-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-021-10625-4
Keywords
- Synchronization
- Topology identification
- Time-varying delayed coupling
- Stochastic multi-weighted complex network
- Graph-theoretic method