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Topology identification of a class of complex spatio-temporal networks with time delay

Topology identification of a class of complex spatio-temporal networks with time delay

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The problem of topology identification is valuable to understand and control complex networks. Furthermore, many complex networks are spatio-temporal in the real world. This study proposes a method to identify the topology of complex spatio-temporal networks with coupling time delay. Adaptive observer and novel updating laws are designed to provide the estimation of the topology. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method. The result is general and can be applicable to contemporary network science and engineering.

References

    1. 1)
      • 1. Liu, Y., Slotine, J., Barabsi, A.: ‘Controllability of complex networks’, Nature, 2011, 473, (7346), pp. 167173.
    2. 2)
      • 2. Nishikawa, T, Motter, A., Lai, Y., et al: ‘Heterogeneity in oscillator networks: are smaller worlds easier to synchronize?’, Phys. Rev. Lett., 2003, 91, (1), p. 014101.
    3. 3)
      • 3. Nepusz, T., Vicsek, T.: ‘Controlling edge dynamics in complex networks’, Nat. Phys., 2012, 8, (7), pp. 568573.
    4. 4)
      • 4. Guan, Z., Wu, Y., Feng, G.: ‘Consensus analysis based on impulsive systems in multiagent networks’, IEEE Trans. Circuits Syst. I: Reg. Pap., 2012, 59, (1), pp. 170178.
    5. 5)
      • 5. Guan, Z., Hu, B., Chi, M., et al: ‘Guaranteed performance consensus in second-order multi-agent systems with hybrid impulsive control’, Automatica, 2014, 50, (9), pp. 24152418.
    6. 6)
      • 6. Guan, Z., Han, G., Li, J., et al: ‘Impulsive multiconsensus of second-order multiagent networks using sampled position data’, IEEE Trans. Neural Netw. Learn. Syst., 2015, 26, (11), pp. 26782688.
    7. 7)
      • 7. Chen, J., Guan, Z., He, D., et al: ‘Multi-consensus for second-order multi-agent systems based on sampled position information’, IET Control Theory Appl., 2015, 9, (3), pp. 358366.
    8. 8)
      • 8. Zhao, X., Hu, B., Guan, Z., et al: ‘Multi-flocking of networked nonholonomic mobile robots with proximity graphs’, IET Control Theory Appl., 2016, 10, (16), pp. 20932099.
    9. 9)
      • 9. Timme, M., Casadiego, J.: ‘Revealing networks from dynamics: an introduction’, J. Phys. A: Math. Theor., 2014, 47, (34), p. 343001.
    10. 10)
      • 10. Boccaletti, S., Latora, V., Moreno, Y., et al: ‘Complex networks: Structure and dynamics’, Phys. Rep., 2006, 424, (4), pp. 175308.
    11. 11)
      • 11. Newman, M.E.J.: ‘The structure and function of complex networks’, SIAM Rev., 2003, 45, (2), pp. 167256.
    12. 12)
      • 12. Timme, M.: ‘Revealing network connectivity from response dynamics’, Phys. Rev. Lett., 2007, 98, (22), p. 224101.
    13. 13)
      • 13. Ren, J., Wang, W., Li, B., et al: ‘Noise bridges dynamical correlation and topology in coupled oscillator networks’, Phys. Rev. Lett., 2010, 104, (5), p. 058701.
    14. 14)
      • 14. Ching, E., Lai, P., Leung, C.: ‘Extracting connectivity from dynamics of networks with uniform bidirectional coupling’, Phys. Rev. E, 2013, 88, (4), p. 042817.
    15. 15)
      • 15. Chen, J., Lu, J., Zhou, J.: ‘Topology identification of complex networks from noisy time series using ROC curve analysis’, Nonlinear Dyn., 2014, 75, (4), pp. 761768.
    16. 16)
      • 16. Shen, Z., Wang, W., Fan, Y., et al: ‘Reconstructing propagation networks with natural diversity and identifying hidden sources’, Nat. Commun., 2014, 5, p. 4323.
    17. 17)
      • 17. Hayden, D., Chang, Y., Goncalves, J., et al: ‘Sparse network identifiability via compressed sensing’, Automatica, 2016, 68, pp. 917.
    18. 18)
      • 18. Nabi-Abdolyousefi, M., Mesbahi, M.: ‘Network identification via node knockout’, IEEE Trans. Autom. Control, 2012, 57, (12), pp. 32143219.
    19. 19)
      • 19. Shahrampour, S., Preciado, V.: ‘Topology identification of directed dynamical networks via power spectral analysis’, IEEE Trans. Autom. Control, 2015, 60, (8), pp. 22602265.
    20. 20)
      • 20. Tirabassi, G., Sevilla-Escoboza, R., Buldú, J., et al: ‘Inferring the connectivity of coupled oscillators from time-series statistical similarity' analysis’, Sci. Rep., 2015, 5, p. 10829.
    21. 21)
      • 21. Besancon, G.: ‘Remarks on nonlinear adaptive observer design’, Syst. Control Lett., 2000, 41, (4), pp. 271280.
    22. 22)
      • 22. Smyshlyaev, A., Orlov, Y., Krstic, M.: ‘Adaptive identification of two unstable PDEs with boundary sensing and actuation’, Int. J. Adaptive Control Signal Process., 2009, 23, (2), pp. 131149.
    23. 23)
      • 23. Yang, C., Guan, Z., Liu, Z., et al: ‘Wide-area multiple line-outages detection in power complex networks’, Int. J. Electr. Power Energy Syst., 2016, 79, pp. 132141.
    24. 24)
      • 24. Yu, D., Righero, M., Kocarev, L.: ‘Estimating topology of networks’, Phys. Rev. Lett., 2006, 97, (18), p. 188701.
    25. 25)
      • 25. Lu, J., Cao, J.: ‘Synchronization-based approach for parameters identification in delayed chaotic neural networks’, Phys. A: Stat. Mech. Appl., 2007, 382, (2), pp. 672682.
    26. 26)
      • 26. Wu, X.: ‘Synchronization-based topology identification of weighted general complex dynamical networks with time-varying coupling delay’, Phys. A: Stat. Mech. Appl., 2008, 387, (4), pp. 9971008.
    27. 27)
      • 27. Yang, X., Wei, T.: ‘Revealing network topology and dynamical parameters in delay-coupled complex network subjected to random noise’, Nonlinear Dyn., 2015, 82, (1–2), pp. 319332.
    28. 28)
      • 28. Liu, H., Lu, J., Lu, J., et al: ‘Structure identification of uncertain general complex dynamical networks with time delay’, Automatica, 2009, 45, (8), pp. 17991807.
    29. 29)
      • 29. Wu, Z., Fu, X.: ‘Structure identification of uncertain dynamical networks coupled with complex-variable chaotic systems’, IET Control Theory Appl., 2013, 7, (9), pp. 12691275.
    30. 30)
      • 30. Wu, X., Sun, Z., Liang, F., et al: ‘Online estimation of unknown delays and parameters in uncertain time delayed dynamical complex networks via adaptive observer’, Nonlinear Dyn., 2013, 73, (3), pp. 17531768.
    31. 31)
      • 31. Mei, J., Jiang, M., Wang, B., et al: ‘Finite-time parameter identification and adaptive synchronization between two chaotic neural networks’, J. Franklin Inst., 2013, 350, (6), pp. 16171633.
    32. 32)
      • 32. Tang, Z., Park, J., Lee, T.: ‘Topology and parameters recognition of uncertain complex networks via nonidentical adaptive synchronization’, Nonlinear Dyn., 2016, 85, pp. 21712181.
    33. 33)
      • 33. Chua, L., Roska, T.: ‘The CNN paradigm’, IEEE Trans. Circuits Syst. I: Fund.l Theory Appl., 1993, 40, (3), pp. 147156.
    34. 34)
      • 34. Wang, J., Wu, H., Huang, T, et al: ‘Pinning control for synchronization of coupled reaction-diffusion neural networks with directed topologies’, IEEE Trans. Syst. Man Cybern.: Syst., 2016, 46, (8), pp. 11091120.
    35. 35)
      • 35. Hu, C., Jiang, H., Teng, Z.: ‘Impulsive control and synchronization for delayed neural networks with reaction-diffusion terms’, IEEE Trans. Neural Netw., 2010, 21, (1), pp. 6781.
    36. 36)
      • 36. Yang, X., Cao, J., Yang, Z.: ‘Synchronization of coupled reaction-diffusion neural networks with time-varying delays via pinning-impulsive controller’, SIAM J. Control Optimiz., 2013, 51, (5), pp. 34863510.
    37. 37)
      • 37. Wu, K., Li, C., Chen, B., et al: ‘Robust H synchronization of coupled partial differential systems with spatial coupling delay’, IEEE Trans. Circuits Syst. II: Express Briefs, 2013, 60, (7), pp. 451455.
    38. 38)
      • 38. Yang, C., Qiu, J., He, H.: ‘Exponential synchronization for a class of complex spatio-temporal networks with space-varying coefficients’, Neurocomputing, 2015, 151, pp. 401407.
    39. 39)
      • 39. Yang, C., Qiu, J., Yi, k., et al: ‘Robust exponential synchronization for a class of master-slave distributed parameter systems with spatially variable coefficients and nonlinear perturbation’, Math. Probr. Eng., 2015, 2015, p. 380903.
    40. 40)
      • 40. Yang, C., Qiu, J., Li, T., et al: ‘Projective exponential synchronization for a class of complex PDDE networks with multiple time delays’, Entropy, 2015, 17, (11), pp. 72987309.
    41. 41)
      • 41. Yang, C., Zhang, A., Chen, X., et al: ‘Stability and stabilization of a delayed PIDE system via SPID control’, Neural Comput. Appl., 2016, DOI: 10.1007/s00521-016-2297-5.
    42. 42)
      • 42. Lu, J.: ‘Global exponential stability and periodicity of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions’, Chaos Solitons Fractals, 2008, 35, (1), pp. 116125.
    43. 43)
      • 43. Tao, G.: ‘A simple alternative to the Barbalat lemma’, IEEE Trans. Autom. Control, 1997, 42, (5), p. 698.
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