Abstract
Multivariate singular spectrum analysis (M-SSA) is a useful tool to detect phase synchronization (PS) without any a priori need for phase estimation. The discriminatory power of M-SSA is often enhanced by using only the time series of the variable that provides the best observability of the dynamics. In the case of a network, however, diverse factors could prevent access to this variable at some nodes. Hence, other variables should be used instead, resulting in a mixed set of variables. The aim of the present work is to investigate, in a systematic way, the impact of using a mixed/incomplete measurement set in the M-SSA of chains of Rössler systems and cord oscillators. Results show that (i) the measurement of some variable from all oscillators does not guarantee detection of PS; (ii) typically one good observable per cluster should be recorded in order to detect PS among such clusters and that (iii) dropping poor variables does not reveal new PS transitions but improves on the resolution of what was already seen with such variables. The procedure is robust to noise.
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References
Stankovski, T., Pereira, T., McClintock, P.V., Stefanovska, A.: Coupling functions: universal insights into dynamical interaction mechanisms. Rev. Mod. Phys. 89(4), 045001 (2017). https://doi.org/10.1103/RevModPhys.89.045001
Osipov, G.V., Kurths, J., Zhou, C.: Synchronization in oscillatory networks. Springer series in synergetics, 1st edn. Springer, Berlin (2007). https://doi.org/10.1007/978-3-540-71269-5
Boccaletti, S., Kurths, J., Osipov, G., Valladares, D., Zhou, C.: The synchronization of chaotic systems. Phys. Rep. 366, 1–101 (2002). https://doi.org/10.1016/S0370-1573(02)00137-0
Mineeja, K.K., Ignatius, R.P.: Spatiotemporal activities of a pulse-coupled biological neural network. Nonlinear Dyn. 92(4), 1881–1897 (2018). https://doi.org/10.1007/s11071-018-4169-2
Aguirre, L.A., Freitas, L.: Control and observability aspects of phase synchronization. Nonlinear Dyn. 91(4), 1–15 (2017). https://doi.org/10.1007/s11071-017-4009-9
Huo, J., Wu, H., Sun, W., Zhang, Z., Wang, L., Dong, J.: Electromechanical coupling dynamics of TBM main drive system. Nonlinear Dyn. 90(4), 2687–2710 (2017). https://doi.org/10.1007/s11071-017-3831-4
Thottil, S.K., Ignatius, R.P.: Nonlinear feedback coupling in HindmarshRose neurons. Nonlinear Dyn. 87(3), 1879–1899 (2017). https://doi.org/10.1007/s11071-016-3160-z
Vasegh, N.: Spatiotemporal and synchronous chaos in accumulated coupled map lattice. Nonlinear Dyn. 89(2), 1089–1097 (2017). https://doi.org/10.1007/s11071-017-3501-6
Wu, W.S., Zhao, Z.S., Zhang, J., Sun, L.K.: State feedback synchronization control of coronary artery chaos system with interval time-varying delay. Nonlinear Dyn. 87(3), 1773–1783 (2017). https://doi.org/10.1007/s11071-016-3151-0
Rosenblum, M.G., Pikovsky, A., Kurths, J.: Phase synchronization in driven and coupled chaotic oscillators. IEEE Trans. Circuits Syst. I Fundam. Theory Appl. 44(10), 874–881 (1997). https://doi.org/10.1109/81.633876
Rosenblum, M.G., Pikovsky, A.S., Kurths, J.: Phase synchronization of chaotic oscillators. Phys. Rev. Lett. 76(11), 1804–1807 (1996). https://doi.org/10.1103/PhysRevLett.76.1804
Pikovsky, A., Rosenblum, M., Kurths, J.: Synchronization: A Universal Concept in Nonlinear Sciences. Cambridge University Press, Cambridge (2001)
Freitas, L., Torres, L.A.B., Aguirre, L.A.: Phase definition to assess synchronization quality of nonlinear oscillators. Phys. Rev. E 97(5), 052202 (2018). https://doi.org/10.1103/PhysRevE.97.052202
Groth, A., Ghil, M.: Multivariate singular spectrum analysis and the road to phase synchronization. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 84(3), 1–12 (2011). https://doi.org/10.1103/PhysRevE.84.036206
Alessio, S.M.: Digital Signal Processing and Spectral Analysis for Scientists. Signals and Communication Technology. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-25468-5
Ghil, M., Allen, M.R., Dettinger, M.D., Ide, K., Kondrashov, D., Mann, M.E., Robertson, aW, Saunders, a, Tian, Y., Varadi, F., Yiou, P.: Advanced spectral methods for climate time series. Rev. Geophys. 40(1), 3.1–3.41 (2002). https://doi.org/10.1029/2001RG000092
Plaut, G., Vautard, R.: Spells of low-frequency oscillations and weather regimes in the northern hemisphere. J. Atmos. Sci. 51(2), 210–236 (1994). https://doi.org/10.1175/1520-0469(1994)051
Vautard, R., Yiou, P., Ghil, M.: Singular-spectrum analysis: a toolkit for short, noisy chaotic signals. Phys. D Nonlinear Phenom. 58, 95–126 (1992). https://doi.org/10.1016/0167-2789(92)90103-T
Broomhead, D., King, G.P.: Extracting qualitative dynamics from experimental data. Phys. D Nonlinear Phenom. 20(2–3), 217–236 (1986)
Broomhead, D., King, G.P.: On the qualitative analysis of experimental dynamical systems. In: Sarkar, S. (ed.) Nonlinear Phenom. Chaos, pp. 113–144. Adam Hilger, Bristol (1986)
Portes, L.L., Aguirre, L.A.: Enhancing multivariate singular spectrum analysis for phase synchronization: the role of observability. Chaos Interdiscip. J. Nonlinear Sci. 26(9), 093112 (2016). https://doi.org/10.1063/1.4963013
Whitney, H.: Differentiable manifolds. Ann. Math. 37(3), 645 (1936). https://doi.org/10.2307/1968482
Letellier, C., Maquet, J., Sceller, L.L., Gouesbet, G., Aguirre, L.A.: On the non-equivalence of observables in phase-space reconstructions from recorded time series. J. Phys. A Math. Gen. 31(39), 7913–7927 (1998). https://doi.org/10.1088/0305-4470/31/39/008
Letellier, C., Aguirre, L.A., Maquet, J.: Relation between observability and differential embeddings for nonlinear dynamics. Phys. Rev. E 71(6), 066213 (2005). https://doi.org/10.1103/PhysRevE.71.066213
Carroll, T.L.: Testing dynamical system variables for reconstruction. Chaos Interdiscip. J. Nonlinear Sci. 28(10), 103117 (2018). https://doi.org/10.1063/1.5049903
Portes, L.L., Benda, R.N., Ugrinowitsch, H., Aguirre, L.A.: Impact of the recorded variable on recurrence quantification analysis of flows. Phys. Lett. A 378(32–33), 2382–2388 (2014). https://doi.org/10.1016/j.physleta.2014.06.014
Letellier, C.: Estimating the shannon entropy: recurrence plots versus symbolic dynamics. Phys. Rev. Lett. 96(25), 254102 (2006). https://doi.org/10.1103/PhysRevLett.96.254102
Hindmarsh, J., Rose, R.: A model of neuronal bursting using three coupled first order differential equations. Proc. R. Soc. Lond. B Biol. Sci. 221(1222), 87–102 (1984)
Groth, A., Ghil, M.: Synchronization of world economic activity. Chaos Interdiscip. J. Nonlinear Sci. 27(12), 127002 (2017). https://doi.org/10.1063/1.5001820
Groth, A., Ghil, M.: Monte Carlo singular spectrum analysis (SSA) revisited: detecting oscillator clusters in multivariate datasets. J. Clim. 28(19), 7873–7893 (2015). https://doi.org/10.1175/JCLI-D-15-0100.1
Feliks, Y., Groth, A., Robertson, A.W., Ghil, M.: Oscillatory climate modes in the Indian Monsoon, North Atlantic, and Tropical Pacific. J. Clim. 26(23), 9528–9544 (2013). https://doi.org/10.1175/JCLI-D-13-00105.1
Aguirre, L.A., Letellier, C.: Investigating observability properties from data in nonlinear dynamics. Phys. Rev. E 83(6), 066209 (2011). https://doi.org/10.1103/PhysRevE.83.066209
Aguirre, L.A., Portes, L.L., Letellier, C.: Structural, dynamical and symbolic observability: from dynamical systems to networks. PLOS ONE 13(10), e0206180 (2018). https://doi.org/10.1371/journal.pone.0206180
Isidori, A.: Nonlinear Control Systems. Communications and Control Engineering. Springer, London (1995). https://doi.org/10.1007/978-1-84628-615-5
Hermann, R., Krener, A.: Nonlinear controllability and observability. IEEE Trans. Autom. Control 22(5), 728–740 (1977). https://doi.org/10.1109/TAC.1977.1101601
Fraedrich, K.: Estimating the dimensions of weather and climate attractors. J. Atmos. Sci. 43(5), 419–432 (1986). https://doi.org/10.1175/1520-0469(1986)043
Takens, F.: Detecting Strange Attractors in Turbulence. In: Rand, D., Young, L.S. (eds.) Dynamical Systems and Turbulence, Warwick 1980. Springer, Berlin (1981). https://doi.org/10.1007/BFb0091924
Golyandina, N., Nekrutkin, V., Zhigljavsky, A.: Analysis of Time Series Structure. C&H/CRC Monographs on Statistics & Applied Probability, vol. 90. Chapman and Hall/CRC, Boca Raton (2001). https://doi.org/10.1201/9781420035841
Elsner, J.B., Tsonis, A.A.: Singular Spectrum Analysis. Springer, Boston (1996). https://doi.org/10.1007/978-1-4757-2514-8
Vautard, R., Ghil, M.: Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series. Phys. D Nonlinear Phenom. 35(3), 395–424 (1989). https://doi.org/10.1016/0167-2789(89)90077-8
Portes, L.L., Aguirre, L.A.: Matrix formulation and singular-value decomposition algorithm for structured varimax rotation in multivariate singular spectrum analysis. Phys. Rev. E 93(5), 052216 (2016). https://doi.org/10.1103/PhysRevE.93.052216
Osipov, G., Pikovsky, A.S., Rosenblum, M.G., Kurths, J.: Phase synchronization effects in a lattice of nonidentical Rössler oscillators. Phys. Rev. E 55(3), 2353–2361 (1997)
Ibañez, C.A.: Algebraic approach for the reconstruction of rossler system from the \(x(3)\)-variable. Revista Mexicana de Física 52(1), 64–69 (2006)
Bickel, P.J., Doksum, K.A.: An analysis of transformations revisited. J. Am. Stat. Assoc. 76(374), 296–311 (1981). https://doi.org/10.1080/01621459.1981.10477649
Box, G.E.P., Cox, D.R.: An analysis of transformations. J. R. Stat. Soc. 26(2), 211–252 (1964)
Acknowledgements
We thank Dr. Paul Castle for helpful discussions
Funding
This study was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq–Brazil, Grant Numbers 302079/2011-4 and 502036/2014-1), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, grant number 3200 1010 015 P8) and Australian Research Council Discovery Grant (DP 180100718).
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Portes, L.L., Aguirre, L.A. Impact of mixed measurements in detecting phase synchronization in networks using multivariate singular spectrum analysis. Nonlinear Dyn 96, 2197–2209 (2019). https://doi.org/10.1007/s11071-019-04917-7
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DOI: https://doi.org/10.1007/s11071-019-04917-7