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Synchronization of random coupling delayed complex networks with random and adaptive coupling strength

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Abstract

Our paper devotes to synchronization issue of random coupling delayed complex networks with adaptive coupling strength as well as random coupling strength via intermittent control. Different from related literature, intermittent control in this paper is aperiodic. Furthermore, we assume coupling delay is a random variable and coupling function is nonlinear. Meanwhile, adaptive and random coupling strength are, respectively, taken into account in our model compared with most existing literature. Some novel sufficient conditions are derived by utilizing Lyapunov method and graph theory. What is more, synchronization of a second-order Kuramoto model, as an application of our theoretical results, is investigated. Meanwhile, some sufficient conditions are given as well. Eventually, some numerical simulations are given to show the effectiveness of results.

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References

  1. Feng, J., Li, N., Zhao, Y., Xu, C., Wang, J.: Finite-time synchronization analysis for general complex dynamical networks with hybrid couplings and time-varying delays. Nonlinear Dyn. 88, 2723–2733 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  2. Ali, M., Gunasekaran, N., Ahn, C., Shi, P.: Sampled-data stabilization for fuzzy genetic regulatory networks with leakage delays. IEEE ACM Trans. Comput. Biol. Bioinform. 15, 271–285 (2018)

    Article  Google Scholar 

  3. Wang, J., Qin, Z., Wu, H., Huang, T.: Finite-time synchronization and \(H_{\infty }\) synchronization of multiweighted complex networks with adaptive state couplings. IEEE Trans. Cybern. (2018). https://doi.org/10.1109/TCYB.2018.2870133

  4. Liu, J., Zhang, Y., Yu, Y., Sun, C.: Fixed-time event-triggered consensus for nonlinear multiagent systems without continuous communications. IEEE Trans. Syst. Man Cybern. (2018). https://doi.org/10.1109/TSMC.2018.2876334

  5. Jordano, P., Bascompte, J., Olesen, J.: Invariant properties in coevolutionary networks of plant–animal interactions. Ecol. Lett. 6, 69–81 (2003)

    Article  Google Scholar 

  6. Jordano, P., Bascompte, J., Olesen, J.: Invariant properties in coevolutionary networks of plant–animal interactions. Math. Biosci. 308, 27–37 (2019)

    Article  MathSciNet  Google Scholar 

  7. Latora, V., Marchiori, M.: Is the Boston subway a small-world network? Physica A 314, 109–113 (2002)

    Article  MATH  Google Scholar 

  8. Cheng, L., Chen, X., Qiu, J., Lu, J., Cao, J.: Aperiodically intermittent control for synchronization of switched complex networks with unstable modes via matrix omega-measure approach. Nonlinear Dyn. 92, 1091–1102 (2018)

    Article  MATH  Google Scholar 

  9. Liu, M., Wu, J., Sun, Y.: Adaptive finite-time outer synchronization between two complex dynamical networks with noise perturbation. Nonlinear Dyn. 89(4), 2967–2977 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  10. Ma, Y., Ma, N., Chen, L.: Synchronization criteria for singular complex networks with Markovian jump and time-varying delays via pinning control. Nonlinear Anal. Hybrid Syst. 29, 85–99 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  11. Gao, H., Lam, J., Chen, G.: New criteria for synchronization stability of general complex dynamical networks with coupling delays. Phys. Lett. A 360(2), 263–273 (2006)

    Article  MATH  Google Scholar 

  12. Wu, Z., Shi, P., Su, H., Chu, J.: Stochastic synchronization of Markovian jump neural networks with time-varying delay using sampled data. IEEE Trans. Cybern. 43(6), 1796–1806 (2013)

    Article  Google Scholar 

  13. Cao, J.: Periodic oscillation and exponential stability of delayed CNNs. Phys. Lett. A 270, 157–163 (2000)

    Article  Google Scholar 

  14. Cao, J., Xiao, M.: Stability and Hopf bifurcation in a simplified BAM neural network with two time delays. IEEE Trans. Neural Netw. 18(2), 416–430 (2007)

    Article  MathSciNet  Google Scholar 

  15. Wei, J., Ruan, S.: Stability and bifurcation in a neural network model with two delays. Phys. D Nonlinear Phenom. 130, 255–272 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  16. Wang, P., Zhang, B., Su, H.: Stabilization of stochastic uncertain complex-valued delayed networks via aperiodically intermittent nonlinear control. IEEE Trans. Syst. Man Cybern. 49, 649–662 (2019)

    Article  Google Scholar 

  17. Lu, H.: Chaotic attractors in delayed neural networks. Phys. Lett. A 298, 109–116 (2002)

    Article  MATH  Google Scholar 

  18. Chen, G., Zhou, J., Liu, Z.: Global synchronization of coupled delayed neural networks and applications to chaotic CNN models. Int. J. Bifurc. Chaos 14(7), 2229–2240 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  19. Wang, X., She, K., Zhong, S., Cheng, J.: Synchronization of complex networks with non-delayed and delayed couplings via adaptive feedback and impulsive pinning control. Nonlinear Dyn. 86, 165–176 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  20. Wu, Y., Wang, C., Li, W.: Generalized quantized intermittent control with adaptive strategy on finite-time synchronization of delayed coupled systems and applications. Nonlinear Dyn. 95, 1361–1377 (2019)

    Article  Google Scholar 

  21. Yu, W., Cao, J., Lü, J.: Global synchronization of linearly hybrid coupled networks with time-varying delay. SIAM J. Appl. Dyn. Syst. 7(1), 108–133 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  22. Wu, Z., Shi, P., Su, H., Chu, J.: Sampled-data exponential synchronization of complex dynamical networks with time-varying coupling delay. IEEE Trans. Neural Netw. Learn. Syst. 24(8), 1177–1187 (2013)

    Article  Google Scholar 

  23. Yang, X., Xu, C., Feng, J., Lu, J.: General synchronization criteria for nonlinear Markovian systems with random delays. J. Frankl. Inst. Eng. Appl. Math. 355, 1394–1410 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  24. Yang, X., Ho, D., Lu, J., Song, Q.: Finite-time cluster synchronization of T–S fuzzy complex networks with discontinuous subsystems and random coupling delays. IEEE Trans. Fuzzy Syst. 23, 2302–2316 (2015)

    Article  Google Scholar 

  25. Yang, X., Cao, J., Lu, J.: Synchronization of coupled neural networks with random coupling strengths and mixed probabilistic time-varying delays. Int. J. Robust Nonlinear Control 23, 2060–2081 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  26. Song, Q.: Synchronization analysis of coupled connected neural networks with mixed time delays. Neurocomputing 72, 3907–3914 (2009)

    Article  Google Scholar 

  27. Wang, J., Wu, H., Huang, T., Ren, S., Wu, J.: Passivity of directed and undirected complex dynamical networks with adaptive coupling weights. IEEE Trans. Neural Netw. Learn. Syst. 28, 1827–1839 (2017)

    Article  MathSciNet  Google Scholar 

  28. Wang, J., Qin, Z., Wu, H., Huang, T., Wei, P.: Analysis and pinning control for output synchronization and \(H_{\infty }\) output synchronization of multiweighted complex networks. IEEE Trans. Cybern. 49(4), 1314–1326 (2019)

    Article  Google Scholar 

  29. Wu, Y., Fu, S., Li, W.: Exponential synchronization for coupled complex networks with time-varying delays and stochastic perturbations via impulsive control. J. Frankl. Inst. Eng. Appl. Math. 356, 492–513 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  30. Wu, Y., Li, Q., Li, W.: Novel aperiodically intermittent stability criteria for Markovian switching stochastic delayed coupled systems. Chaos 28, 113117 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  31. Wu, Y., Liu, Y., Li, W.: Finite-time stabilization of coupled systems on networks with time-varying delays via periodically intermittent control. Asian J. Control 21(6), 1–12 (2019)

    MathSciNet  Google Scholar 

  32. Liu, Y., Li, W., Feng, J.: The stability of stochastic coupled systems with time-varying coupling and general topology structure. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 4189–4200 (2018)

    Article  Google Scholar 

  33. Zheng, C., Sun, N.: Mode-dependent stochastic synchronization criteria for Markovian hybrid neural networks with random coupling strengths. J. Frankl. Inst. Eng. Appl. Math. 354(13), 5559–5588 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  34. Wu, Y., Gong, Y., Wang, Q.: Random coupling strength-induced synchronization transitions in neuronal network with delayed electrical and chemical coupling. Physica A 421, 347–354 (2015)

    Article  Google Scholar 

  35. Zheng, C., Shan, Q., Wei, Z.: Stochastic synchronization for an array of hybrid neural networks with random coupling strengths and unbounded distributed delays. Neurocomputing 273, 22–36 (2018)

    Article  Google Scholar 

  36. Wang, J.: Synchronization of complex networks with random coupling strengths and mixed probabilistic time-varying coupling delays using sampled data. In: Abstract and applied analysis, vol. 845304 (2014)

  37. Yang, X., Cao, J., Lu, J.: Synchronization of Markovian coupled neural networks with nonidentical node-delays and random coupling strengths. IEEE Trans. Neural Netw. Learn. Syst. 23(1), 60–71 (2012)

    Article  Google Scholar 

  38. Wu, D., Zhu, S., Luo, X., Wu, L.: Effects of adaptive coupling on stochastic resonance of small-world networks. Phys. Rev. E 84, 021102 (2011)

    Article  Google Scholar 

  39. Hu, C., Jiang, H.: Pinning synchronization for directed networks with node balance via adaptive intermittent control. Nonlinear Dyn. 80, 295–307 (2015)

    Article  MATH  Google Scholar 

  40. Xia, W., Cao, J.: Pinning synchronization of delayed dynamical networks via periodically intermittent control. Chaos 19, 013120 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  41. Zhang, G., Shen, Y.: Exponential synchronization of delayed memristor-based chaotic neural networks via periodically intermittent control. Neural Netw. 55, 1–10 (2014)

    Article  MATH  Google Scholar 

  42. Li, C., Feng, G., Liao, X.: Stabilization of nonlinear systems via periodically intermittent control. IEEE Trans. Circuits Syst. II Expr. Briefs 54, 1019–1023 (2007)

    Article  Google Scholar 

  43. Qiu, J., Cheng, L., Chen, X., Lu, J., He, H.: Semi-periodically intermittent control for synchronization of switched complex networks: a mode-dependent average Dwell time approach. Nonlinear Dyn. 83, 1757–1771 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  44. Liu, X., Chen, T.: Synchronization of complex networks via aperiodically intermittent pinning control. IEEE Trans. Autom. Control 60(12), 3316–3321 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  45. Zhang, W., Li, C., Huang, T., Xiao, M.: Synchronization of neural networks with stochastic perturbation via aperiodically intermittent control. Neural Netw. 75, 105–111 (2015)

    Article  MATH  Google Scholar 

  46. Gan, Q.: Exponential synchronization of generalized neural networks with mixed time-varying delays and reaction-diffusion terms via aperiodically intermittent control. Chaos 27(1), 013113 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  47. Mao, X.: Stochastic Differential Equations and Applications. Horwood Publishing Limited, Cambridge (1997)

    MATH  Google Scholar 

  48. Zhou, P., Cai, S.: Pinning synchronization of complex directed dynamical networks under decentralized adaptive strategy for aperiodically intermittent control. Nonlinear Dyn. 90(1), 287–299 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  49. Li, Y., Shuai, Z.: Global-stability problem for coupled systems of differential equations on networks. J. Differ. Equ. 248(1), 1–20 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  50. West, D.B.: Introduction to Graph Theory. Prentice Hall, Upper Saddle River (1996)

    MATH  Google Scholar 

  51. Guo, B., Wu, Y., Xiao, Y., Zhang, C.: Graph-theoretic approach to synchronizing stochastic coupled systems with time-varying delays on networks via periodically intermittent control. Appl. Math. Comput. 331, 341–357 (2010)

    MathSciNet  Google Scholar 

  52. Wang, P., Jin, W., Su, H.: Synchronization of coupled stochastic complex-valued dynamical networks with time-varying delays via aperiodically intermittent adaptive control. Chaos 28(4), 043114 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  53. Liu, X., Chen, T.: Synchronization of linearly coupled networks with delays via aperiodically intermittent pinning control. IEEE Trans. Neural Netw. Learn. Syst. 26, 2396–2407 (2015)

    Article  MathSciNet  Google Scholar 

  54. Peron, T., Ji, P., Rodrigues, F., Kurths, J.: Effects of assortative mixing in the second-order Kuramoto model. Phys. Rev. E. 91, 052805 (2015)

    Article  MathSciNet  Google Scholar 

  55. Filatrell, G., Nielsen, A., Pedersen, N.: Analysis of a power grid using a Kuramoto-like model. Eur. Phys. J. B 61, 485–491 (2008)

    Article  Google Scholar 

  56. Guan, Z., Yang, S., Yao, J.: Stability analysis and \(H_{\infty }\) control for hybrid complex dynamical networks with coupling delays. Int. J. Robust Nonlinear Control 22(2), 205–222 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  57. Feng, J., Yu, F., Zhao, Y.: Exponential synchronization of nonlinearly coupled complex networks with hybrid time-varying delays via impulsive control. Nonlinear Dyn. 85(1), 621–632 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  58. Rodrigues, F., Peron, T., Ji, P., Kurths, J.: The Kuramoto model in complex networks. Phys. Rep. 610, 1–98 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  59. Lu, Z., Takeuchi, Y.: Global asymptotic-behavior in single-species discrete diffusion-systems. J. Math. Biol. 32(1), 66–77 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  60. Wu, Y., Chen, B., Li, W.: Synchronization of stochastic coupled systems via feedback control based on discrete-time state observations. Nonlinear Anal. Hybrid Syst. 26, 68–85 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  61. Liu, Y., Jia, W., Li, W.: Stabilization problem of stochastic time-varying coupled systems with time delay and feedback controls. Appl. Anal. 97(11), 1983–2000 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  62. Guo, B., Xiao, Y., Zhang, C.: Synchronization analysis of stochastic coupled systems with time delay on networks by periodically intermittent control and graph-theoretic method. Nonlinear Anal. Hybrid Syst. 30, 118–133 (2018)

    Article  MathSciNet  MATH  Google Scholar 

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Acknowledgements

The authors really appreciate the valuable comments of the editors and reviewers. This work was supported by Shandong Province Natural Science Foundation (Nos. ZR2018MA005, ZR2018MA020, ZR2017MA008); the Key Project of Science and Technology of Weihai (No. 2014DXGJMS08) and the Innovation Technology Funding Project in Harbin Institute of Technology (No. HIT.NSRIF.201703).

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Appendix

Appendix

1.1 Proof of Theorem 1

Proof

To start with, let \(V(e,t)=\sum _{k=1}^{N}c_{k}V_{k}(e_{k},t)\), where \( V_{k}(e_{k},t)=e_{k}^{\mathrm {T}}e_{k}+e^{-\rho t}\left( a(t)-a_{0}\right) ^{2}\). Considering digraph \(({\mathcal {G}},B)\) is strongly connected, in view of Proposition 2.1 in [49], we can easily avail that \(c_{k}>0\). And we have

$$\begin{aligned}&{\mathcal {L}}V_{k}(e_{k}(t),t)\\&\quad =2e_{k}^{\mathrm {T}}(t)\Big [{\tilde{f}}_{k}\left( e_{k}(t),e_{k}(t-\tau _{1}(t)),t\right) \\&\qquad +\beta (t)a(t)\sum _{h=1}^{N}a_{kh}{\tilde{H}}_{kh}\left( e_{k}(t-\tau _{2}(t)),e_{h}(t-\tau _{2}(t))\right) \nonumber \\&\qquad +a(t)(1-\beta (t))\sum _{h=1}^{N}a_{kh}{\tilde{H}}_{kh}(e_{k}(t-\tau _{3}(t)),\\&\qquad \times e_{h}(t-\tau _{3}(t)))+u_{k}(t)\Big ]+\left| {\tilde{g}}_{k}(e_{k}(t),t)\right| ^{2}\nonumber \\&\qquad -\rho e^{-\rho t}(a(t)-a_{0})^{2}+ 2e^{-\rho t}(a(t)-a_{0}){\dot{a}}(t), \end{aligned}$$

where the differential operator \({\mathcal {L}}\) can be discovered in [25]. Based on Assumption 2, the following inequalities can be yielded that

$$\begin{aligned}&2e_{k}^{\mathrm {T}}(t){\tilde{f}}_{k}\left( e_{k}(t),e_{k}(t-\tau _{1}(t)),t \right) \nonumber \\&\quad \le (2\varGamma _{k}+\varLambda _{k})|e_{k}(t)|^{2}+\varLambda _{k}|e_{k}(t-\tau _{1}(t))|^{2} \end{aligned}$$
(20)

and

$$\begin{aligned}&2e_{k}^{\mathrm {T}}(t)a(t)\beta (t)\sum _{h=1}^{N}a_{kh}{\tilde{H}}_{kh}\left( e_{k}(t-\tau _{2}(t)),e_{h}(t-\tau _{2}(t))\right) \nonumber \\&\quad \le 2 a(t)\beta (t)\sum _{h=1}^{N}a_{kh}b_{kh}\left[ |e_{k}(t)|^{2}+|e_{k}(t-\tau _{2}(t))|^{2}\right] \nonumber \\&\qquad +\, a(t)\beta (t)\sum _{h=1}^{N}a_{kh}b_{kh}\nonumber \\&\qquad \times \left[ |e_{h}(t-\tau _{2}(t))|^{2}-|e_{k}(t-\tau _{2}(t))|^{2}\right] . \end{aligned}$$
(21)

The following inequality can be obtained by analogical derivation of (21)

$$\begin{aligned}&2 a(t)(1-\beta (t))e_{k}^{\mathrm {T}}(t)\sum _{h=1}^{N}a_{kh}{\tilde{H}}_{kh}\left( e_{k}(t-\tau _{3}(t)),\right. \nonumber \\&\quad \left. e_{h}(t-\tau _{3}(t))\right) \nonumber \\&\quad \le 2 a(t)(1-\beta (t))\sum _{h=1}^{N}a_{kh}b_{kh}\left[ |e_{k}(t)|^{2}\right. \nonumber \\&\qquad \left. +\,|e_{k}(t-\tau _{3}(t))|^{2}\right] \nonumber \\&\qquad +\,a(t)(1-\beta (t))\sum _{h=1}^{N}a_{kh}b_{kh}\left[ |e_{h}(t-\tau _{3}(t))|^{2}\right. \nonumber \\&\qquad \left. -\,|e_{k}(t-\tau _{3}(t))|^{2}\right] . \end{aligned}$$
(22)

In view of (10), one can acquire that

$$\begin{aligned}&e^{-\rho t}{\dot{a}}(t)(a(t)-a_{0})\nonumber \\&\quad \le -a(t)b_{k}|e_{k}(t)|^2-a(t)b_{k}|e_{k}(t-\tau _{2}(t))|^{2}\nonumber \\&\qquad -a(t)b_{k}|e_{k}(t-\tau _{3}(t))|^{2}\nonumber \\&\qquad +a_{0}\sum _{k=1}^{N}b_{k}\left[ |e_{k}(t)|^2+|e_{k}(t-\tau _{2}(t))|^{2}\right. \nonumber \\&\qquad \left. +|e_{k}(t-\tau _{3}(t))|^{2}\right] . \end{aligned}$$
(23)

According to (4), when \(t\in [t_{m},s_{m})\), one has \(e_{k}^{\mathrm {T}}(t)u_{k}(t)=-d_{k}e_{k}^{\mathrm {T}}(t)e_{k}(t)\). In what follows, we can obtain by applying (20)–(23)

$$\begin{aligned}&{\mathbb {E}}{\mathcal {L}}V(e(t),t)\nonumber \\&\quad \le \sum _{k=1}^{N}c_{k}\left( 2\varGamma _{k}+\varLambda _{k}+\delta ^{2}_{k}-2d_{k}\right) {\mathbb {E}}|e_{k}(t)|^{2}\nonumber \\&\qquad +\,\sum _{k=1}^{N}c_{k}\varLambda _{k}{\mathbb {E}}|e_{k}(t-\tau _{1}(t))|^{2} \nonumber \\&\qquad +\,2a_{0}\sum _{k=1}^{N}c_{k}\sum _{h=1}^{N}b_{h}{\mathbb {E}}\left[ |e_{h}(t)|^{2}+|e_{h}(t-\tau _{2}(t))|^{2}\right. \nonumber \\&\qquad \left. +\,|e_{h}(t-\tau _{3}(t))|^{2}\right] \nonumber \\&\qquad +\,{\mathbb {E}}\beta (t)a(t)\sum _{k=1}^{N}c_{k}\sum _{h=1}^{N}a_{kh}b_{kh}\left[ |e_{h}(t-\tau _{2}(t))|^{2}\right. \nonumber \\&\qquad \left. -\,|e_{k}(t-\tau _{2}(t))|^{2}\right] \nonumber \\&\qquad +\,{\mathbb {E}}(1-\beta (t))a(t)\sum _{k=1}^{N}c_{k}\sum _{h=1}^{N}a_{kh}b_{kh}\left[ |e_{h}(t-\tau _{3}(t))|^{2}\right. \nonumber \\&\qquad \left. -\,|e_{k}(t-\tau _{3}(t))|^{2}\right] \nonumber \\&\qquad -\,\sum _{k=1}^{N}c_{k}\rho e^{-\rho t}(a(t)-a_{0})^{2}. \end{aligned}$$
(24)

According to Theorem 2.2 in [49], we can get

$$\begin{aligned}&\sum _{k=1}^{N}\sum _{h=1}^{N}c_{k}a_{kh}b_{kh}\left[ |e_{h}(t-\tau _{2}(t))|^{2}-|e_{k}(t-\tau _{2}(t))|^{2}\right] \\&\quad =\sum _{{\mathcal {Q}}\in {\mathbb {Q}}}W({\mathcal {Q}})\sum _{(s,r)\in E(C_{{\mathcal {Q}}})}\\&\qquad \times \left[ |e_{s}(t-\tau _{2}(t))|^{2}-|e_{r}(t-\tau _{2}(t))|^{2}\right] , \end{aligned}$$

where \(C_{{\mathcal {Q}}}\) denotes the directed cycle of \({\mathcal {Q}}\). We can get that

$$\begin{aligned} \sum _{(s,r)\in E(C_{{\mathcal {Q}}})}\left[ |e_{s}(t-\tau _{2}(t))|^{2}-|e_{r}(t-\tau _{2}(t))|^{2}\right] =0. \end{aligned}$$

Therefore, one has

$$\begin{aligned}&\sum _{k=1}^{N}\sum _{h=1}^{N}c_{k}a_{kh}b_{kh}\\&\quad \left[ |e_{h}(t-\tau _{2}(t))|^{2}-|e_{k}(t-\tau _{2}(t))|^{2}\right] =0. \end{aligned}$$

It is easy to get that

$$\begin{aligned}&\beta (t)a(t)\sum _{k=1}^{N}c_{k}\sum _{h=1}^{N}a_{kh}b_{kh}\\&\quad \times \left[ |e_{h}(t-\tau _{2}(t))|^{2}-|e_{k}(t-\tau _{2}(t))|^{2}\right] =0. \end{aligned}$$

By a similar method, we can obtain

$$\begin{aligned}&a(t)(1-\beta (t))\sum _{k=1}^{N}c_{k}\sum _{h=1}^{N}a_{kh}b_{kh}\\&\quad \times \left[ |e_{h}(t-\tau _{3}(t))|^{2}-|e_{k}(t-\tau _{3}(t))|^{2}\right] =0. \end{aligned}$$

Therefore, one get

$$\begin{aligned}&{\mathbb {E}}{\mathcal {L}}V(e(t),t)\nonumber \\&\quad \le \sum _{k=1}^{N}c_{k}\left( 2\varGamma _{k}+\varLambda _{k}+\delta ^{2}_{k}-2d_{k}+2\frac{{\overline{c}}}{{\underline{c}}}a_{0}Nb_{k}\right) {\mathbb {E}}|e_{k}(t)|^{2}\nonumber \\&\qquad -\sum _{k=1}^{N}c_{k}\rho e^{-\rho t}(a(t)-a_{0})^{2}\nonumber \\&\qquad +\sum _{k=1}^{N}c_{k}\varLambda _{k}{\mathbb {E}}|e_{k}(t-\tau _{1}(t))|^{2}\nonumber \\&\qquad +2\sum _{k=1}^{N}c_{k}\left( \frac{{\overline{c}}}{{\underline{c}}}a_{0}Nb_{k}\right) \nonumber \\&\qquad \times {\mathbb {E}}\left[ |e_{k}(t-\tau _{2}(t))|^{2}+|e_{k}(t-\tau _{3}(t))|^{2}\right] \nonumber \\&\quad \le -\lambda {\mathbb {E}}\left[ \sum _{k=1}^{N}c_{k}\left( V_{k}(e_{k}(t))+e^{-\rho t}(a(t)-a_{0})^{2}\right) \right] \nonumber \\&\qquad +\eta \sum _{k=1}^{N}c_{k}{\mathbb {E}}\left[ |e_{k}(t-\tau _{1}(t))|^{2}\right. \nonumber \\&\qquad \left. +|e_{k}(t-\tau _{2}(t))|^{2}+|e_{k}(t-\tau _{3}(t))|^{2}\right] . \end{aligned}$$
(25)

For any \(t\in [s_{m},t_{m+1})\), we can similarly compute the \({\mathcal {L}}V_{k}(e_{k}(t),t)\) as follows:

$$\begin{aligned} {\mathbb {E}}{\mathcal {L}}V_{k}(e_{k}(t),t)\le & {} \left( 2\varGamma _{k}+\varLambda _{k}+\delta ^{2}_{k}\right) {\mathbb {E}}|e_{k}(t)|^{2}\\&+\,\varLambda _{k}{\mathbb {E}}|(e_{k}(t-\tau _{1}(t))|^{2}\\&+\,2{\mathbb {E}}a(t)\beta (t)\sum _{h=1}^{N}a_{kh}b_{kh}\\&\times \,\left[ |e_{k}(t)|^{2}+|e_{k}(t-\tau _{2}(t))|^{2}\right] \\&+\,{\mathbb {E}}a(t)\beta (t)\sum _{h=1}^{N}a_{kh}b_{kh}\\&\times \,\left[ |e_{h}(t-\tau _{2}(t))|^{2}-|e_{k}(t-\tau _{2}(t))|^{2}\right] \\&+\,2{\mathbb {E}}a(t)(1-\beta (t))\sum _{h=1}^{N}a_{kh}b_{kh}\\&\times \,\left[ |e_{k}(t)|^{2}+|e_{k}(t-\tau _{3}(t))|^{2}\right] \\&+\,{\mathbb {E}}a(t)(1-\beta (t))\sum _{h=1}^{N}a_{kh}b_{kh}\\&\times \,\left[ |e_{h}(t-\tau _{3}(t))|^{2}-|e_{k}(t-\tau _{3}(t))|^{2}\right] \\&-\,{\mathbb {E}}a(t)b_{k}\left[ |e_{k}(t)|^{2}+|e_{k}(t-\tau _{2}(t))|^{2}\right. \\&+\left. |e_{k}(t-\tau _{3}(t))|^{2}\right] \\&+\,{\mathbb {E}}a_{0}\sum _{k=1}^{N}b_{k}\left[ |e_{k}(t)|^{2}+|e_{k}(t-\tau _{2}(t))|^{2}\right. \\&+\left. |e_{k}(t-\tau _{3}(t))|^{2}\right] \\&-\,\rho e^{-\rho t}(a(t)-a_{0})^{2}. \end{aligned}$$

Thus, one get

$$\begin{aligned}&{\mathbb {E}}{\mathcal {L}}V(e(t),t)\nonumber \\&\quad \le \sum _{k=1}^{N}c_{k}\left( 2\varGamma _{k}+\varLambda _{k}+\delta ^{2}_{k}+\frac{{\bar{c}}}{{\underline{c}}}a_{0}Nb_{k}\right) {\mathbb {E}}|e_{k}(t)|^{2}\nonumber \\&\qquad -\sum _{k=1}^{N}c_{k}\rho e^{-\rho t}(a(t)-a_{0})^{2}\nonumber \\&\qquad +\sum _{k=1}^{N}c_{k}\varLambda _{k}{\mathbb {E}}|e_{k}(t-\tau _{1}(t))|^{2}\nonumber \\&\qquad +\sum _{k=1}^{N}c_{k}\left( \frac{{\bar{c}}}{{\underline{c}}}a_{0}Nb_{k}\right) {\mathbb {E}}\left[ |e_{k}(t-\tau _{2}(t))|^{2}\right. \nonumber \\&\qquad \left. +|e_{k}(t-\tau _{3}(t))|^{2}\right] \nonumber \\&\quad \le {\bar{\lambda }}{\mathbb {E}}\left( \sum _{k=1}^{N}c_{k}V_{k}(e_{k}(t))+e^{-\rho t}(a(t)-a_{0})^{2}\right) \nonumber \\&\qquad +\eta \sum _{k=1}^{N}c_{k}{\mathbb {E}}\left[ |e_{k}(t-\tau _{1}(t))|^{2}\right. \nonumber \\&\qquad \left. +|e_{k}(t-\tau _{2}(t))|^{2}+|e_{k}(t-\tau _{3}(t))|^{2}\right] . \end{aligned}$$
(26)

Then, combining (25) with (26), we can conclude that

$$\begin{aligned}&{\mathbb {E}}{\mathcal {L}}V(e(t),t)\le \left\{ \begin{array}{ll} \displaystyle -\lambda {\mathbb {E}}V(e(t),t)+\eta \sum _{i=1}^{3}{\mathbb {E}}V\left( e(t-\tau _{i}(t)),t-\tau _{i}(t)\right) , &{}\quad t\in [t_{m},s_{m}), \\ \displaystyle {\bar{\lambda }}{\mathbb {E}}V(e(t),t)+\eta \sum _{i=1}^{3}{\mathbb {E}}V(e(t-\tau _{i}(t)),t-\tau _{i}(t)), &{} \quad t\in [s_{m},t_{m+1}). \end{array} \right. \end{aligned}$$

Therefore, one get

$$\begin{aligned}&{\mathbb {E}}{\mathcal {L}}V(e(t),t)\le \left\{ \begin{array}{ll} \displaystyle -\lambda {\mathbb {E}}V(e(t),t)+3\eta \sup _{t-\tau \le s\le t}\{{\mathbb {E}}V{(e(s),s)}\}, &{}\quad t\in [t_{m},s_{m}),\\ \displaystyle {\bar{\lambda }}{\mathbb {E}}V(e(t),t)+3\eta \sup _{t-\tau \le s\le t}\{{\mathbb {E}}V(e(s),s)\}, &{}\quad t\in [s_{m},t_{m+1}). \end{array} \right. \end{aligned}$$

On account of (11) and Lemma 1, we can obtain that

$$\begin{aligned}&{\mathbb {E}}V(e(t),t)\le \sup _{-\tau \le s\le 0}\{{\mathbb {E}}V(e(s),s)\} \exp \{-\sigma t\},\nonumber \\&\quad t\ge 0. \end{aligned}$$
(27)

What is more, by virtue of the definition of V(e(t), t), it yields

$$\begin{aligned} V(e(t),t)\ge \min _{k\in {\mathcal {N}}}\left\{ c_{k}\right\} |e(t)|^{2}. \end{aligned}$$
(28)

Consequently, according to (27) and (28), we can deduce

$$\begin{aligned} \limsup _{t\rightarrow \infty }\frac{1}{t}\mathrm {ln}\left( {\mathbb {E}}|e(t)|^{2}\right) \le -\sigma <0. \end{aligned}$$

Hence, in view of Definition 1, systems (1) and (2) with adaptive coupling strength achieve exponential synchronization in mean square. Then, the proof is completed. \(\square \)

1.2 Proof of Theorem 2

Proof

According to Assumption 2 and (8), error system (5) can be converted to

$$\begin{aligned} \text {d}{e}_{k}(t)= & {} \Big [{\tilde{f}}_{k}(e_{k}(t),e_{k}(t-\tau _{1}(t)),t)\\&+\,\beta (t){\hat{a}}_{0}\sum _{h=1}^{N}a_{kh}{\tilde{H}}_{kh} (e_{k}(t-\tau _{2}(t)),e_{h}(t-\tau _{2}(t))) \\&+\,\beta (t)(a(t)-{\hat{a}}_{0})\sum _{h=1}^{N}a_{kh}{\tilde{H}}_{kh}(e_{k}(t-\tau _{2}(t)),\\&\quad e_{h}(t-\tau _{2}(t)))\\&+\,(1-\beta (t)){\hat{a}}_{0}\sum _{h=1}^{N}a_{kh}{\tilde{H}}_{kh}(e_{k}(t-\tau _{3}(t)),\\&\quad e_{h}(t-\tau _{3}(t)))\\&+\,(1-\beta (t))(a(t)-{\hat{a}}_{0})\sum _{h=1}^{N}a_{kh}{\tilde{H}}_{kh}(e_{k}(t-\tau _{3}(t)),\\&\quad e_{h}(t-\tau _{3}(t)))\\&+\,u_{k}(t)\Big ]\text {d}t+{\tilde{g}}_{k}(e_{k}(t),t)\text {d}B(t). \end{aligned}$$

Next, let \(V(e,t)=\sum _{h=1}^{N}c_{k}V_{k}(e_{k},t)\), where \(V_{k}(e_{k},t)=|e_{k}|^{p}\), \(c_{k}\) stands for the cofactor of the kth diagonal element of Laplacian matrix of digraph \(({\mathcal {G}}, B)\), where \(B=(a_{kh}b_{kh})_{N\times N}\). What is more, by virtue of Proposition 2.1 in [49], we can discover that \(c_{k}>0\).

By using the Young’s inequality,

$$\begin{aligned} |a|^{p} |b|^{q}\le \frac{p}{p+q}|a|^{p+q}+ \frac{q}{p+q}|b|^{p+q} \end{aligned}$$

and Assumption 2, it is easy to see that

$$\begin{aligned}&|e_{k}(t)|^{p-2}e_{k}^{\mathrm {T}}(t){\tilde{f}}_{k}(e_{k}(t),e_{k}(t-\tau _{1}(t)),t)\nonumber \\&\quad \le |e_{k}(t)|^{p-1}(\varGamma _{k}|e_{k}(t)|+\varLambda _{k}|e_{k}(t-\tau _{1}(t))|)\nonumber \\&\quad \le \frac{p\varGamma _{k}+(p-1)\varLambda _{k}}{p}|e_{k}(t)|^{p}+\frac{\varLambda _{k}}{p}|e_{k}(t-\tau _{1}(t))|^{p}. \end{aligned}$$
(29)

Furthermore, similar to (29), it yields

$$\begin{aligned}&|e_{k}(t)|^{p-2}e_{k}^{\mathrm {T}}(t){\tilde{H}}_{kh}(e_{k}(t-\tau _{2}(t)),e_{h}(t-\tau _{2}(t))) \\&\quad \le b_{kh}\left[ \frac{2(p-1)}{p}|e_{k}(t)|^{p}+\frac{1}{p}|e_{k}(t-\tau _{2}(t))|^{p}\right. \\&\qquad \left. +\frac{1}{p}|e_{h}(t-\tau _{2}(t))|^{p}\right] . \end{aligned}$$

Therefore, when \(t\in [t_{m},s_{m})\), the following inequality can be deduced

$$\begin{aligned}&{\mathbb {E}}{\mathcal {L}}V_{k}(e_{k}(t),t)\\&\quad \le \left[ p\varGamma _{k}+(p-1)\varLambda _{k}+{\hat{a}}_{0}\sum _{h=1}^{N}2a_{kh}b_{kh}(p-1)\right. \\&\qquad \left. -pd_{k}+\frac{p(p-1)}{2}\delta _{k}^{2}\right] {\mathbb {E}}|e_{k}(t)|^{p}\\&\qquad +\varLambda _{k}{\mathbb {E}}|e_{k}(t-\tau _{1}(t))|^{p}\\&\qquad +2\beta _{0}{\hat{a}}_{0}\sum _{h=1}^{N}a_{kh}b_{kh}{\mathbb {E}}|e_{k}(t-\tau _{2}(t))|^{p}\\&\qquad +\beta _{0}{\hat{a}}_{0}\sum _{h=1}^{N}a_{kh}b_{kh}\left[ {\mathbb {E}}|e_{h}(t-\tau _{2}(t))|^{p}\right. \\&\qquad \left. -{\mathbb {E}}|e_{k}(t-\tau _{2}(t))|^{p}\right] \\&\qquad +2(1-\beta _{0}){\hat{a}}_{0}\sum _{h=1}^{N}a_{kh}b_{kh}{\mathbb {E}}|e_{k}(t-\tau _{3}(t))|^{p}\\&\qquad +(1-\beta _{0}){\hat{a}}_{0}\sum _{h=1}^{N}a_{kh}b_{kh}\left[ {\mathbb {E}}|e_{h}(t-\tau _{3}(t))|^{p}\right. \\&\qquad \left. -{\mathbb {E}}|e_{k}(t-\tau _{3}(t))|^{p}\right] . \end{aligned}$$

In virtue of Theorem 2.2 in [49], we can see that

$$\begin{aligned}&\beta _{0}{\hat{a}}_{0}\sum _{k=1}^{N}\sum _{h=1}^{N}c_{k}a_{kh}b_{kh}\left[ {\mathbb {E}}|e_{h}(t-\tau _{2}(t))|^{p}\right. \\&\quad \left. -{\mathbb {E}}|e_{k}(t-\tau _{2}(t))|^{p}\right] =0,\\&(1-\beta _{0}){\hat{a}}_{0}\sum _{k=1}^{N}\sum _{h=1}^{N}c_{k}a_{kh}b_{kh}\left[ {\mathbb {E}}|e_{h}(t-\tau _{3}(t))|^{p}\right. \\&\quad \left. -{\mathbb {E}}|e_{k}(t-\tau _{3}(t))|^{p}\right] =0. \end{aligned}$$

Besides, one can deduce as follows:

$$\begin{aligned}&{\mathbb {E}}{\mathcal {L}}V(e(t),t)\nonumber \\&\quad \le \sum _{k=1}^{N}c_{k}\left[ p\varGamma _{k}+(p-1)\varLambda _{k}\right. \nonumber \\&\qquad +{\hat{a}}_{0}\sum _{h=1}^{N}2a_{kh}b_{kh}(p-1)-pd_{k}\nonumber \\&\qquad \left. +\frac{p(p-1)}{2}\delta _{k}^{2}\right] {\mathbb {E}}|e_{k}(t)|^{2} \nonumber \\&\qquad +\sum _{k=1}^{N}c_{k}\varLambda _{k}{\mathbb {E}}|e_{k}(t-\tau _{1}(t))|^{p}\nonumber \\&\qquad +\sum _{k=1}^{N}2c_{k}\beta _{0}{\hat{a}}_{0}\sum _{h=1}^{N}a_{kh}b_{kh}{\mathbb {E}}|e_{k}(t-\tau _{2}(t))|^{p}\nonumber \\&\qquad +\sum _{k=1}^{N}2c_{k}(1-\beta _{0}){\hat{a}}_{0}\sum _{h=1}^{N}a_{kh}b_{kh}{\mathbb {E}}|e_{k}(t-\tau _{3}(t))|^{p}. \end{aligned}$$
(30)

Hence, it yields by (30)

$$\begin{aligned} {\mathbb {E}}{\mathcal {L}}V(e(t),t)\le & {} -\varepsilon _{1}{\mathbb {E}}V(e(t),t)\\&+\,\varLambda {\mathbb {E}}V(e(t-\tau _{1}(t)),t-\tau _{1}(t)) \\&+\,{\bar{b}}{\mathbb {E}}V(e(t-\tau _{2}(t)),t-\tau _{2}(t))\\&+\,{\hat{b}}{\mathbb {E}}V(e(t-\tau _{3}(t)),t-\tau _{3}(t)). \end{aligned}$$

Similarly, when \(t\in [s_{m},t_{m+1})\), one has

$$\begin{aligned}&{\mathbb {E}}{\mathcal {L}}V(e(t),t)\nonumber \\&\quad \le \sum _{k=1}^{N}c_{k}\left[ p\varGamma _{k}+(p-1)\varLambda _{k}\right. \nonumber \\&\qquad +{\hat{a}}_{0}\sum _{h=1}^{N}2(p-1)a_{kh}b_{kh}\nonumber \\&\qquad \left. +\frac{p(p-1)}{2}\delta _{k}^{2}\right] {\mathbb {E}}|e_{k}(t)|^{2} \nonumber \\&\qquad +\sum _{k=1}^{N}c_{k}\varLambda _{k}{\mathbb {E}}|e_{k}(t-\tau _{1}(t))|^{p}\nonumber \\&\qquad +\sum _{k=1}^{N}2c_{k}\beta _{0}{\hat{a}}_{0}\sum _{h=1}^{N}a_{kh}b_{kh}{\mathbb {E}}|e_{k}(t-\tau _{2}(t))|^{p}\nonumber \\&\qquad +\sum _{k=1}^{N}2c_{k}(1-\beta _{0}){\hat{a}}_{0}\sum _{h=1}^{N}a_{kh}b_{kh}{\mathbb {E}}|e_{k}(t-\tau _{3}(t))|^{p}\nonumber \\&\quad \le \varepsilon _{2}{\mathbb {E}}V(e(t),t)+\varLambda {\mathbb {E}}V(e(t-\tau _{1}(t)),t-\tau _{1}(t)) \nonumber \\&\qquad +{\bar{b}}{\mathbb {E}}V(e(t-\tau _{2}(t)),t-\tau _{2}(t))\nonumber \\&\qquad +{\hat{b}}{\mathbb {E}}V(e(t-\tau _{3}(t)),t-\tau _{3}(t)). \end{aligned}$$
(31)

Consequently, combining (30) with (31), we have

$$\begin{aligned}&{\mathbb {E}}{\mathcal {L}}V(e(t),t)\\&\quad \le \left\{ \begin{array}{ll} \displaystyle -\varepsilon _{1}{\mathbb {E}}V(e(t),t)+(\varLambda +{\bar{b}}+{\hat{b}})\sup _{-\tau \le s\le 0}\{{\mathbb {E}}V{(e(s),s)}\}, &{}\quad t\in [t_{m},s_{m}),\\ \displaystyle \varepsilon _{2}{\mathbb {E}}V(e(t),t)+(\varLambda +{\bar{b}}+{\hat{b}})\sup _{-\tau \le s\le 0}\{{\mathbb {E}}V(e(s),s)\}, &{}\quad t\in [s_{m},t_{m+1}). \end{array} \right. \end{aligned}$$

In view of (12) and Lemma 1, we can obtain that

$$\begin{aligned} {\mathbb {E}}V(e(t),t)\le \sup _{-\tau \le s\le 0}\{{\mathbb {E}}V(e(s),s)\} \exp \{-\sigma t\}, t\ge 0. \end{aligned}$$

Moreover, we obtain

$$\begin{aligned} \min _{k\in {\mathcal {N}}}\left\{ c_{k}\right\} |e(t)|^{p}\le V(e(t),t). \end{aligned}$$

Thus, it is clear that

$$\begin{aligned} \limsup _{t\rightarrow \infty }\frac{1}{t}\mathrm {ln}({\mathbb {E}}|e(t)|^{p})\le -\sigma <0, \end{aligned}$$

which indicates systems (1) and (2) with random coupling strength reach pth moment exponential synchronization by virtue of Definition 1. Then, the proof is completed. \(\square \)

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Wu, Y., Li, Y. & Li, W. Synchronization of random coupling delayed complex networks with random and adaptive coupling strength. Nonlinear Dyn 96, 2393–2412 (2019). https://doi.org/10.1007/s11071-019-04930-w

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