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Anti-Synchronization of Markovian Jumping Stochastic Chaotic Neural Networks with Mixed Time Delays

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Abstract

The anti-synchronization control is investigated for a class of uncertain stochastic chaotic neural networks with both Markovian jump parameters and mixed delays. The mixed delays consists of discrete and distributed time-varying delays. First, by combining the Lyapunov method and a generalized Halanay-type inequality for stochastic differential equations, a delay-dependent criterion is established to guarantee the state variables of the discussed stochastic chaotic neural networks to be globally exponential anti-synchronized. Next, by utilizing a novel lemma and the Jensen integral inequality, a delay-dependent criterion is proposed to achieve the globally stochastic robust anti-synchronization. With some parameters being fixed in advance, the proposed conditions are all expressed in terms of linear matrix inequalities, which can be solved numerically by employing the standard Matlab LMI toolbox package. Finally, two examples are proposed to demonstrate the effectiveness and usefulness of the proposed results.

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References

  1. C.T.H. Baker, E. Buckwar, Exponential stability in \(p\)-th mean of solutions, and of convergent Euler-type solutions, of stochastic delay differential equations. J. Comput. Appl. Math. 184(2), 404–427 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  2. A. Friedman, Stochastic differential equations and applications (Academic Press, New York, 1976)

    MATH  Google Scholar 

  3. J. Fu, H. Zhang, T. Ma, Delay-probability-distribution-dependent robust stability analysis for stochastic neural networks with time-varying delay. Prog. Nat. Sci. 19, 1333–1340 (2009)

    Article  MathSciNet  Google Scholar 

  4. M. Gao, X. Lou, B. Cui, Robust exponential stability of markovian jumping neural networks with time-varying delay. Int. J. Neural Syst. 17(3), 207–218 (2007)

    Article  Google Scholar 

  5. K. Gu, V.L. Kharitonov, J. Chen, Stability of Time-Delay Systems (Birkhauser, Boston, 2003)

    Book  MATH  Google Scholar 

  6. X. Guan, C. Chen, Delay-dependent guaranteed cost control for T-S fuzzy systems with time delays. IEEE Trans. Fuzzy Syst. 12(2), 236–249 (2004)

    Article  MATH  Google Scholar 

  7. X. Li, J. Cao, Delay-independent exponential stability of stochastic Cohen-Grossberg neural networks with time-varying delays and reaction-diffusion terms. Nonlinear Dyn. 50, 363–371 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  8. Z. Liu, H. Zhang, Q. Zhang, Novel stability analysis for recurrent neural networks with multiple delays via line integral-type L-K functional. IEEE Trans. Neural Netw. 21(11), 1710–1718 (2010)

    Article  Google Scholar 

  9. X. Mao, Exponential stability of stochastic delay interval systems with Markovian switching. IEEE Trans. Autom. Contr. 47(10), 1604–1612 (2002)

    Article  Google Scholar 

  10. J. Meng, X. Wang, Robust anti-synchronization of a class of delayed chaotic neural networks. Chaos 17, 023113 (2007)

    Article  MathSciNet  Google Scholar 

  11. L. Pecora, T. Carroll, Synchronization in chaotic systems. Phys. Rev. Lett. 64, 821–824 (1990)

    Article  MATH  MathSciNet  Google Scholar 

  12. Q. Song, J. Cao, Passivity of uncertain neural networks with both leakage delay and time-varying delay. Nonlinear Dyn. 67(2), 1695–1707 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  13. F. Ren, J. Cao, Anti-synchronization of stochastic perturbed delayed chaotic neural networks. Neural Comput. Appl. 18, 515–521 (2009)

    Article  Google Scholar 

  14. Y. Tang, J. Fang, Q. Miao, On the exponential synchronization of stochastic jumping chaotic neural networks with mixed delays and sector-bounded non-linearities. Neurocomputing 72, 1694–1701 (2009)

    Article  Google Scholar 

  15. P. Tino, M. Cernansky, L. Benuskova, Markovian architectural bias of recurrent neural netwoks. IEEE Trans. Neural Netw. 15(1), 6–15 (2004)

    Article  Google Scholar 

  16. Z. Wang, Y. Liu, X. Liu, Exponential stabilization of a class of stochastic system with Markovian jump parameters and mode-dependent mixed time-delays. IEEE Trans. Autom. Contr. 55(7), 1656–1662 (2010)

    Article  Google Scholar 

  17. Z. Wang, H. Zhang, B. Jiang, LMI-based approach for global asymptotic stability analysis of recurrent neural networks with various delays and structures. IEEE Trans. Neural Netw. 22(7), 1032–1045 (2011)

    Article  Google Scholar 

  18. Z. Wang, H. Zhang, P. Li, An LMI approach to stability analysis of reaction-diffusion Cohen-Grossberg neural networks concerning Dirichlet boundary conditions and distributed delays. IEEE Trans. Syst. Man Cyber. B Cyber. 40(6), 1596–1606 (2010)

    Google Scholar 

  19. S. Wen, Z. Zeng, T. Huang, H\(_\infty \) filtering for neutral systems with mixed delays and multiplicative noises. IEEE Trans. Circ. Syst. II 59(11), 820–824 (2012)

    Google Scholar 

  20. S. Wen, Z. Zeng, T. Huang, Reliable H\(_\infty \) filter design for a class of mixed-delay markovian jump systems with stochastic nonlinearities and multiplicative noises via delay-partitioning method. Int. J. Control Autom. Syst. 10(4), 711–720 (2012)

    Article  Google Scholar 

  21. C. Yuan, J. Lygeros, Stabilization of a class of stochastic differential equations with Markovian switching. Syst. Control Lett. 54, 819–833 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  22. B. Zhang, S. Xu, Y. Zou, Improved delay-dependent exponential stability criteria for discrete-time recurrent neural networks with time-varying delays. Neurocomputing 72(1–3), 321–330 (2008)

    Article  Google Scholar 

  23. H. Zhang, Y. Wang, Stability analysis of Markovian jumping stochastic Cohen-Grossberg neural networks with mixed time delays. IEEE Trans. Neural Netw. 19(2), 366–370 (2008)

    Article  Google Scholar 

  24. H. Zhang, Z. Liu, G.B. Huang, Z. Wang, Novel weighting-delay-based stability criteria for recurrent neural networkswith time-varying delay. IEEE Trans. Neural Netw. 21(1), 91–106 (2010)

    Article  Google Scholar 

  25. H. Zhang, T. Ma, G.B. Huang, Z. Wang, Robust global exponential synchronization of uncertain chaotic delayed neural networks via dual-stage impulsive control. IEEE Trans. Syst. Man Cyber. B Cyber. 40(3), 831–844 (2010)

    Article  Google Scholar 

  26. H. Zhang, Y. Quan, Modeling identification and control of a class of nonlinear system. IEEE Trans. Fuzzy Syst. 9(2), 349–354 (2001)

    Article  Google Scholar 

  27. H. Zhang, Z. Wang, D. Liu, Global asymptotic stability of recurrent neural networks with multiple time-varying delays. IEEE Trans. Neural Netw. 19(5), 855–873 (2008)

    Article  MathSciNet  Google Scholar 

  28. H. Zhao, Q. Zhang, Global impulsive exponential anti-synchronization of delayed chaotic neural networks. Neurocomputing 74, 563–567 (2011)

    Article  Google Scholar 

  29. C.-D. Zheng, H. Zhang, Z. Wang, New less-conservative stability results for uncertain stochastic neural networks with fewer slack variables. Int. J. Robust. Nonlinear Control 23, 731–753 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  30. Q. Zhou, B. Chen, C. Lin, H. Li, Mean square exponential stability for uncertain delayed stochastic neural networks with Markovian jump parameters. Circuits Syst. Signal Process. 29(2), 331–348 (2008)

    Article  MathSciNet  Google Scholar 

  31. W. Zhou, D. Tong, Y. Gao, C. Ji, H. Su, Mode and delay-dependent adaptive exponential synchronization in pth moment for stochastic delayed neural networks with Markovian switching. IEEE Trans. Neural Netw. Learn. Syst. 23(3), 662–668 (2012)

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Natural Science Foundation of China 61034005, 61074073, 61273022, Program for New Century Excellent Talents in University of China (NCET-10-0306), and the Fundamental Research Funds for the Central Universities under Grants N110504001 and N100104102.

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Correspondence to Cheng-De Zheng.

Appendices

Appendix I

1.1 Proof of Theorem 1

Proof

Consider the following Lyapunov-Krasovskii functional:

$$\begin{aligned} V(t,e_t,i)&= e(t)^TP_ie(t)+2\sum ^n_{j=1}q_{ji}\int ^{e_j(t)}_0\left[ f_j(s)-\gamma _js\right] \mathrm{d}s\nonumber \\&+2\sum ^n_{j=1}u_{ji}\int ^{e_j(t)}_0\left[ \sigma _j s\!-\! {f}_j(s)\right] \mathrm{d}s\!+\!\int ^t_{t-\tau _i(t)}\left[ e(s)^TSe(s)\!+\!f(e(s))^TRf(e(s)) \right] \mathrm{d}s,\nonumber \\ \end{aligned}$$
(15)

where \(Q_i=\hbox { diag}\{q_{1i},q_{2i},\ldots ,q_{ni}\},U_i=\hbox { diag}\{u_{1i},u_{2i},\ldots ,u_{ni}\}.\)

It can be easily verified that \(V(t,e_t,i)\) is a nonnegative function over \([-\bar{\tau },+\infty ).\) Evaluating the weak infinitesimal operator of \(V(t,e_t,i)\) along the trajectory of system (8), we have that

$$\begin{aligned} \mathrm{d}V(t,e_t,i)=\pounds V(t,e_t,i)\mathrm{d}t+V_{e}(t,e_t,i)\rho _i(t)\mathrm{d}\omega (t). \end{aligned}$$
(16)

where

$$\begin{aligned}&\pounds V(t,e_t,i)\nonumber \\&\quad = 2\left[ e{(t)^T}{P_i}+(f(e(t))-\Gamma e(t))^T{Q_i}+(\Sigma e(t)-f{{(e(t))}^T}{U_i} \right] \nonumber \\&\qquad \times \left[ -(C_i(t)-{X_i})e(t)+{Y_i}e(t-\tau _i(t))+A_i(t)f(e(t)) \right] +\sum \limits _{j=1}^N{{{\pi }_{ij}}e{(t)^T}{P_j}e(t)}\nonumber \\&\qquad +\,2\sum \limits _{k=1}^N{{{\pi }_{ik}}}\sum \limits _{j=1}^n{\left\{ {{q}_{jk}}\int _{0}^{{e_j}(t)}{\left[ {{f}_j}(s)-{{\gamma }_j}s \right] \mathrm{d}s}+{{u}_{jk}}\int _{0}^{{e_j}(t)}{\left[ {{\sigma }_j}s-{{f}_j}(s) \right] \mathrm{d}s} \right\} }\nonumber \\&\qquad -\,(1-{{{\dot{\tau }}}_i}(t))\left[ e{{(t-\tau _i(t))}^T}Se(t-\tau _i(t))+f{{(e(t-\tau _i(t)))}^T}Rf(e(t-\tau _i(t)))\right] \nonumber \\&\qquad +\,e{(t)^T}Se(t)+f{{(e(t))}^T}Rf(e(t))+\sum \limits _{j=1}^N{{{\pi }_{ij}}{\tau _j}(t)}\left[ e{{(t-\tau _i(t))}^T}Se(t-\tau _i(t))\right. \nonumber \\&\qquad \left. +\,f{{(e(t-\tau _i(t)))}^T}Rf(e(t-\tau _i(t)))\right] +\frac{1}{2}\hbox { trace}\left[ \rho _i(t)^T\frac{\partial ^2}{\partial e^2}V(t,e_t,i)\rho _i(t)\right] .\nonumber \\ \end{aligned}$$
(17)

On the other hand, it follows from (5) that

$$\begin{aligned}&2\sum \limits _{k=1}^N{{{\pi }_{ik}}}\sum \limits _{j=1}^n{{{q}_{jk}}\int _{0}^{{e_j}(t)}{\left[ {{f}_j}(s)-{{\gamma }_j}s \right] \mathrm{d}s}}&\nonumber \\&\quad \le 2\sum \limits _{k=1}^N\pi '_{ik}\sum \limits _{j=1}^n{{{q}_{jk}}\int _{0}^{{e_j}(t)}{\left[ {{f}_j}(s)-{{\gamma }_j}s \right] \mathrm{d}s}}&\nonumber \\&= 2\sum \limits _{k=1}^N\pi '_{ik}e{(t)^T}{Q_{k}}(\Sigma -\Gamma )e(t),&\end{aligned}$$
(18)
$$\begin{aligned}&2\sum \limits _{k=1}^N{{{\pi }_{ik}}}\sum \limits _{j=1}^n{{{u}_{jk}}\int _{0}^{{e_j}(t)}{\left[ {e_j}s-{{f}_j}(s) \right] \mathrm{d}s}}&\nonumber \\&\quad \le 2\sum \limits _{k=1}^N\pi '_{ik}\sum \limits _{j=1}^n{{{u}_{jk}}\int _{0}^{{e_j}(t)}{\left[ {e_j}s-{{f}_j}(s) \right] \mathrm{d}s}}&\nonumber \\&=2\sum \limits _{k=1}^N\pi '_{ik}e{(t)^T}{U_{k}}(\Sigma -\Gamma )e(t).&\end{aligned}$$
(19)

Furthermore, the following inequalities hold for any \(t>0\)

$$\begin{aligned}&\sum \limits _{j=1}^N{{{\pi }_{ij}}{\tau _j}(t)}\left[ e{{(t-\tau _i(t))}^T}Se(t-\tau _i(t)) +f{{(e(t-\tau _i(t)))}^T}Rf(e(t-\tau _i(t))) \right] \\&\quad \le \sum \limits _{j=1}^N\pi '_{ik}\bar{\tau }_j\left[ e{{(t-\tau _i(t))}^T}Se(t-\tau _i(t)) +f{{(e(t-\tau _i(t)))}^T}Rf(e(t-\tau _i(t))) \right] . \end{aligned}$$

For any \(j=1,2,\ldots ,n,\) it follows from (5) that

$$\begin{aligned} 0\le \frac{\mathrm{d}(f_j(e_j(t))-\lambda _je_j(t))}{\mathrm{d} e_j(t)}\le \sigma _j-\lambda _j, \ \ 0\le \frac{\mathrm{d}(\sigma _je_j(t)-f_j(e_j(t)))}{\mathrm{d} e_j(t)}\le \sigma _j-\lambda _j. \end{aligned}$$

Thus we have that

$$\begin{aligned}&\frac{1}{2}\frac{\partial ^2}{\partial e^2}V(t,e_t,i)&\nonumber \\&= P_i+U_i\times \hbox { diag}\left\{ \frac{d(\sigma _1e_1(t)-f_1(e_1(t)))}{d e_1(t)},\ldots ,\frac{d(\sigma _ne_n(t)-f_n(e_n(t)))}{d e_n(t)}\right\}&\nonumber \\&+Q_i\times \hbox { diag}\left\{ \frac{d(f_1(e_1(t))-\lambda _1e_1(t))}{d e_1(t)},\ldots ,\frac{d(f_n(e_n(t))-\lambda _ne_n(t))}{d e_n(t)}\right\}&\nonumber \\&\le \widetilde{P}_i.&\end{aligned}$$
(20)

In addition, from (5) the following matrix inequalities hold for any positive diagonal matrices \(W_i,Z_i\) with compatible dimensions (see [4, 22])

$$\begin{aligned}&0\le -e(t)^T\Sigma \Gamma W_ie(t)+e(t)^TW_i(\Sigma +\Gamma )f(e(t))-f(e(t))^TW_if(e(t)),&\end{aligned}$$
(21)
$$\begin{aligned}&0\le -e(t-\tau _i(t))^T\Sigma \Gamma Z_i e(t-\tau _i(t))&\nonumber \\&+e(t-\tau _i(t))^TZ_i(\Sigma +\Gamma )f(e(t-\tau _i(t)))-f(e(t-\tau _i(t)))^TZ_if(e(t-\tau _i(t))).&\nonumber \\ \end{aligned}$$
(22)

According to Assumption 1 and Lemma 1, for any positive scalar \(\varepsilon _i\) we get that

$$\begin{aligned}&\frac{1}{2}\hbox { trace}\left\{ \rho _i(t)^T\frac{\partial ^2}{\partial e^2}V(t,e_t,i)\rho _i(t)\right\} \nonumber \\&\quad = \frac{1}{2}[D_i(t)e(t)+E_i(t)e(t-\tau _i(t))]^TV_{ee}(t,e_t,i)[D_i(t)e(t)+E_i(t)e(t-\tau _i(t))]\nonumber \\&\quad \le \left[ D_ie(t)+E_ie(t-\tau _i(t))+F_iG_i(t)(K_{5i}e(t)+K_{6i}e(t-\tau _i(t)))\right] ^T\nonumber \\&\qquad \times \widetilde{P}_i\left[ D_ie(t)\!+\!E_ie(t-\tau _i(t))\!+\!F_iG_i(t)(K_{5i}e(t)\!+\!K_{6i}e(t\!-\!\tau _i(t)))\right] \nonumber \\&\quad \le [D_ie(t)+E_ie(t-\tau _i(t))]^T\widetilde{P}_i[D_ie(t)+E_ie(t-\tau _i(t))]\nonumber \\&\qquad +[D_ie(t)\!+\!E_ie(t\!-\!\tau _i(t))]^T\widetilde{P}_iF_i(\varepsilon _i I\!-\!F_i^T\widetilde{P}_iF_i)^{-1}F_i^T\widetilde{P}_i[D_ie(t)\!+\!E_ie(t\!-\!\tau _i(t))]\nonumber \\&\qquad +\varepsilon _i[K_{5i}e(t)+K_{6i}e(t-\tau _i(t))]^T[K_{5i}e(t)+K_{6i}e(t-\tau _i(t))]. \end{aligned}$$
(23)

It follows from (17)–(23) that

$$\begin{aligned} \pounds V(t,e_t,i)&\le \zeta _i(t)^T\Omega _i(t)\zeta _i(t)+e(t-\tau _i(t))^TH_ie(t-\tau _i(t))\nonumber \\&+f(e(t-\tau _i(t)))^TJ_if(e(t-\tau _i(t))), \end{aligned}$$
(24)

where

$$\begin{aligned} \zeta _i(t)= \hbox { col}\left\{ e(t),e(t-\tau _i(t)),f(e(t)),f(e(t-\tau _i(t)))\right\} , \end{aligned}$$
$$\begin{aligned} \Omega _i(t)= \left[ \begin{array}{cccc} \psi _{1i}+\Omega _{1i}(t)&{}\psi _{3i}+\Omega _{2i}(t)&{}\psi _{5i}+D_i^T\widehat{P}_iE_i&{}\bar{P}_iB_i(t)-\epsilon _i K_{1i}^TK_{3i}\\ *&{}\Omega _{3i}(t)-W_i+R&{}\widehat{Y}_i&{}\Omega _{4i}(t)\\ *&{}*&{}\psi _{7i}+E_i^T\widehat{P}_iE_i&{}\frac{1}{2}Z_i(\Sigma +\Gamma )\\ *&{}*&{}*&{}\psi _{8i}+\epsilon _i K_{3i}^TK_{3i}\\ \end{array}\right] , \end{aligned}$$

with

$$\begin{aligned} \Omega _{1i}(t)= -\hbox { sys}(\bar{P}_iC_i(t))+\epsilon _i K_{1i}^TK_{1i}^T+D_i^T\widehat{P}_iD_i,\ \widetilde{X}_i=\bar{P}_iX_i,\ \widetilde{Y}_i=\bar{P}_iY_i, \end{aligned}$$
$$\begin{aligned} \Omega _{2i}(t)= \bar{P}_iA_i(t)-C_i(t)^T(Q_i-U_i)-\epsilon _i K_{1i}^TK_{2i},\ \widehat{X}_i=(Q_i-U_i)X_i, \end{aligned}$$
$$\begin{aligned} \Omega _{3i}(t)= \hbox { sys}((Q_i-U_i)A_i(t))+\epsilon _i K_{2i}^TK_{2i},\ \ \widehat{Y}_i=(Q_i-U_i)Y_i, \end{aligned}$$
$$\begin{aligned} \Omega _{4i}(t)=(Q_i-U_i)B_i(t)+\epsilon _i K_{2i}^TK_{3i},\ \ \widehat{P}_i=\widetilde{P}_iF_i(\varepsilon _i I-F_i^T\widetilde{P}_iF_i)^{-1}F_i^T\widetilde{P}_i. \end{aligned}$$

Now, by (13), it is easy to see that there exists a scalar \(\lambda >1\) such that

$$\begin{aligned} \left[ \begin{array}{ccc} \widetilde{\Psi }_i&{}\mathcal {A}_iF_i&{}\mathcal {B}\widetilde{P}_iF_i\\ *&{}-\epsilon _i I&{}0\\ *&{}*&{}-\varepsilon _i I+F_i^T\widetilde{P}_iF_i\\ \end{array}\right] <0, \end{aligned}$$
(25)

where

$$\begin{aligned} \widetilde{\Psi }_i= \left[ \begin{array}{cccc} \lambda M_i+\psi _{1i}+\psi _{2i}&{}\psi _{3i}+\psi _{4i}&{}\psi _{5i}&{}\bar{P}_iB_i-\epsilon _i K_{1i}^TK_{3i}\\ *&{}\psi _{6i}-W_i+R&{}\widehat{Y}_i&{}(Q_i-U_i)B_i+\epsilon _i K_{2i}^TK_{3i}\\ *&{}*&{}\psi _{7i}&{}\frac{1}{2}Z_i(\Sigma +\Gamma )\\ *&{}*&{}*&{}\psi _{8i}+\epsilon _i K_{3i}^TK_{3i}\\ \end{array}\right] . \end{aligned}$$

Applying Schur complements to (25) results in that

$$\begin{aligned}&\left[ \begin{array}{cccc} \psi _{ai}&{}\psi _{3i}+\psi _{4i}&{}\psi _{5i}+D_i^T\widehat{P}_iE_i&{}\bar{P}_iB_i-\epsilon _i K_{1i}^TK_{3i}\\ *&{}\psi _{6i}-W_i+R&{}\widehat{Y}_i&{}(Q_i-U_i)B_i+\epsilon _i K_{2i}^TK_{3i}\\ *&{}*&{}\psi _{7i}+E_i^T\widehat{P}_iE_i&{}\frac{1}{2}Z_i(\Sigma +\Gamma )\\ *&{}*&{}*&{}\psi _{8i}+\epsilon _i K_{3i}^TK_{3i}\\ \end{array}\right] \nonumber \\&\quad +\,\epsilon _i^{-1}\left[ \begin{array}{cccc} \bar{P}_iF_i\\ (Q_i-U_i)F_i\\ 0\\ 0\\ \end{array}\right] \left[ \begin{array}{cccc} \bar{P}_iF_i\\ (Q_i-U_i)F_i\\ 0\\ 0\\ \end{array}\right] ^T+\epsilon _i\left[ \begin{array}{cccc} -K_{1i}^T\\ K_{2i}^T\\ 0\\ K_{3i}^T\\ \end{array}\right] \left[ \begin{array}{cccc} -K_{1i}^T\\ K_{2i}^T\\ 0\\ K_{3i}^T\\ \end{array}\right] ^T<0,\nonumber \\ \end{aligned}$$
(26)

where \(\psi _{ai}=\lambda M_i+\psi _{1i}+\psi _{2i}+D_i^T\widehat{P}_iD_i.\)

Using Assumption 1 and Lemma 1, for any positive scalar \(\epsilon _i\) we have that

$$\begin{aligned}&\left[ \begin{array}{cccc} -\hbox { sys}(\bar{P}_i\Delta C_i(t))&{}\bar{P}_i\Delta A_i(t)-\Delta C_i(t)^T(Q_i-U_i)&{}0&{}\bar{P}_i\Delta B_i(t)\\ *&{}\hbox { sys}((Q_i-U_i)\Delta A_i(t))&{}0&{}(Q_i-U_i)\Delta B_i(t)\\ *&{}*&{}0&{}0\\ *&{}*&{}*&{}0\\ \end{array}\right] \\&\quad = \left[ \begin{array}{cccc} \bar{P}_iF_i\\ (Q_i-U_i)F_i\\ 0\\ 0\\ \end{array}\right] G_i(t)\left[ \begin{array}{cccc} -K_{1i}^T\\ K_{2i}^T\\ 0\\ K_{3i}^T\\ \end{array}\right] ^T+\left[ \begin{array}{cccc} -K_{1i}^T\\ K_{2i}^T\\ 0\\ K_{3i}^T\\ \end{array}\right] G_i(t)^T\left[ \begin{array}{cccc} \bar{P}_iF_i\\ (Q_i-U_i)F_i\\ 0\\ 0\\ \end{array}\right] ^T\\&\quad = \left[ \begin{array}{cccc} \bar{P}_iF_i\\ (Q_i-U_i)F_i\\ 0\\ 0\\ \end{array}\right] G_i(t)\epsilon _i^{-1}G_i(t)^T\left[ \begin{array}{cccc} \bar{P}_iF_i\\ (Q_i-U_i)F_i\\ 0\\ 0\\ \end{array}\right] ^T\!\!+\!\!\left[ \begin{array}{cccc} -K_{1i}^T\\ K_{2i}^T\\ 0\\ K_{3i}^T\\ \end{array}\right] \epsilon _i\left[ \begin{array}{cccc} -K_{1i}^T\\ K_{2i}^T\\ 0\\ K_{3i}^T\\ \end{array}\right] ^T\\&\qquad -\left\{ \epsilon _i^{-1}\left[ \begin{array}{cccc} \bar{P}_iF_i\\ (Q_i-U_i)F_i\\ 0\\ 0\\ \end{array}\right] G_i(t)-\left[ \begin{array}{cccc} -K_{1i}^T\\ K_{2i}^T\\ 0\\ K_{3i}^T\\ \end{array}\right] \right\} \epsilon _i\\&\qquad \times \left\{ \epsilon _i^{-1}\left[ \begin{array}{cccc} \bar{P}_iF_i\\ (Q_i-U_i)F_i\\ 0\\ 0\\ \end{array}\right] G_i(t)-\left[ \begin{array}{cccc} -K_{1i}^T\\ K_{2i}^T\\ 0\\ K_{3i}^T\\ \end{array}\right] \right\} ^T\\&\quad \le \epsilon _i^{-1}\left[ \begin{array}{cccc} \bar{P}_iF_i\\ (Q_i-U_i)F_i\\ 0\\ 0\\ \end{array}\right] \left[ \begin{array}{cccc} \bar{P}_iF_i\\ (Q_i-U_i)F_i\\ 0\\ 0\\ \end{array}\right] ^T+\epsilon _i\left[ \begin{array}{cccc} -K_{1i}^T\\ K_{2i}^T\\ 0\\ K_{3i}^T\\ \end{array}\right] \left[ \begin{array}{cccc} -K_{1i}^T\\ K_{2i}^T\\ 0\\ K_{3i}^T\\ \end{array}\right] ^T. \end{aligned}$$

This together with (26) provides that

$$\begin{aligned} \Omega _i(t)+\hbox { diag}\{\lambda M_i,\ 0,\ 0,\ 0\}<0. \end{aligned}$$

By this inequality and (24), we have that

$$\begin{aligned} \mathrm{d}V(t,e_t,i)&= \pounds V(t,e_t,i)\mathrm{d}t+V_{e}(t,e_t,i)[D_i(t)e(t)+E_i(t)e(t-\tau _i(t))]\mathrm{d}\omega (t)\\&< \big [-\lambda e(t)^TM_ie(t)+e(t-\tau _i(t))^TH_ie(t-\tau _i(t))+f(e(t-\tau _i(t)))^T\\&\times \, J_if(e(t\!-\!\tau _i(t)))\big ]\mathrm{d}t\!+\!V_{e}(t,e_t,i)[D_i(t)e(t)\!+\!E_i(t)e(t\!-\!\tau _i(t))]\mathrm{d}\omega (t). \end{aligned}$$

Taking the mathematical expectations on both sides of (16), from above inequality we obtain that

$$\begin{aligned} \mathrm{d}\mathbf {E}\{V(t,e_t,i)\}&= \mathbf {E}\pounds V(t,e_t,i)\mathrm{d}t\\&+\,\mathbf {E}\{V_{e}(t,e_t,i)[D_i(t)e(t)+E_i(t)e(t-\tau _i(t))]\mathrm{d}\omega (t)\}\\&< \big [-\lambda e(t)^TM_ie(t)+e(t-\tau _i(t))^TH_ie(t-\tau _i(t))\\&+f(e(t-\tau _i(t)))^TJ_if(e(t-\tau _i(t)))\big ]\mathrm{d}t. \end{aligned}$$

By integrating above inequality from \(t-\tau _i(t)\) to \(t,\) we get that

$$\begin{aligned}&\mathbf {E}\{V(t,e_t,i)\}-\mathbf {E}\{V(t,e_{t-\tau _i(t)},i)\}\\&\quad =\int _{t-\tau _i(t)}^t \mathbf {E}\{V(s,e_s,i)\}\mathrm{d}s\\&\quad <-\lambda \int _{t-\tau _i(t)}^t e(s)^TM_ie(s)\mathrm{d}s\\&\qquad +\int _{t-\tau _i(t)}^t \left[ e(s\!-\!\tau _i(s))^TH_ie(s\!-\!\tau _i(s))\!+\!f(e(s-\tau _i(s)))^TJ_if(e(s\!-\!\tau _i(s)))\right] \mathrm{d}s. \end{aligned}$$

It follows that

$$\begin{aligned}&\mathbf {E}\left[ \frac{\mathrm{d}V(t,e_t,i)}{\mathrm{d}t} \right] +\beta \mathbf {E}\left[ V(t,e_t,i)-V(t,e_{t-\tau _i(t)},i)\right] \nonumber \\&\quad < -\lambda e(t)^TM_ie(t)+e(t-\tau _i(t))^TH_ie(t-\tau _i(t))\nonumber \\&\qquad +f(e(t-\tau _i(t)))^TJ_if(e(t-\tau _i(t)))-\beta \lambda \int _{t-\tau _i(t)}^t e(s)^TM_ie(s)\mathrm{d}s\nonumber \\&\qquad +\beta \int _{t-\tau _i(t)}^t \left[ e(s\!-\!\tau _i(s))^TH_ie(s\!-\!\tau _i(s))\!+\!f(e(s\!-\!\tau _i(s)))^TJ_if(e(s\!-\!\tau _i(s)))\right] \mathrm{d}s.\nonumber \\ \end{aligned}$$
(27)

In view of (10) and (11), we derive that

$$\begin{aligned} -e(t)^TM_ie(t)\le -\alpha e(t)^T\widetilde{P}_ie(t), \end{aligned}$$
(28)
$$\begin{aligned} -\beta \int _{t-\tau _i(t)}^t e(s)^TM_ie(s)ds\le -\alpha \int _{t-\tau _i(t)}^t \left[ e(s)^TSe(s)+f(e(s))^TRf(e(s))\right] \mathrm{d}s. \end{aligned}$$
(29)

Noting that

$$\begin{aligned}&2\sum ^n_{j=1}q_{ji}\int ^{e_j(t)}_0\left[ f_j(s)-\lambda _js\right] \mathrm{d}s\\&\quad \le 2\sum ^n_{j=1}q_{ji}\int ^{e_j(t)}_0(\sigma _j-\lambda _j)s\mathrm{d}s=e(t)^TQ_i(\Sigma -\Gamma )e(t),\\&2\sum ^n_{j=1}u_{ji}\int ^{e_j(t)}_0\left[ \sigma _j s- {f}_j(s)\right] \mathrm{d}s\\&\quad \le 2\sum ^n_{j=1}u_{ji}\int ^{e_j(t)}_0(\sigma _j-\lambda _j)s\mathrm{d}s=e(t)^TU_i(\Sigma -\Gamma )e(t). \end{aligned}$$

Thus we have that

$$\begin{aligned} \mathbf {E}\{V(t,e_t,i)\}\le e(t)^T\widetilde{P}_ie(t)+\int _{t-\tau _i(t)}^t \left[ e(s)^TSe(s)+f(e(s))^TRf(e(s))\right] \mathrm{d}s. \end{aligned}$$

This together with (28)–(29) yields that

$$\begin{aligned} -e(t)^TM_ie(t)-\beta \int _{t-\tau _i(t)}^te(s)^TM_ie(s)\mathrm{d}s\le -\alpha \mathbf {E}\{V(t,e_t,i)\}. \end{aligned}$$
(30)

Moreover, \(\mathbf {E}\{V(t,e_t,i)\}\ge e(t)^TP_ie(t),\) therefore it follows from (5) and (12) that

$$\begin{aligned}&e(t-\tau _i(t))^TH_ie(t-\tau _i(t))+f(e(t-\tau _i(t)))^TJ_if(e(t-\tau _i(t)))\nonumber \\&\quad \le e(t-\tau _i(t))^T\left[ H_i+\Theta J_i\Theta \right] e(t-\tau _i(t))\nonumber \\&\quad \le \frac{\alpha }{1+\beta \bar{\tau }}e(t-\tau _i(t))^TP_ie(t-\tau _i(t))\nonumber \\&\quad \le \frac{\alpha }{1+\beta \bar{\tau }}\mathbf {E}\{V(t,e_{t-\tau _i(t)},i)\}. \end{aligned}$$
(31)

Thus we obtain that

$$\begin{aligned}&\int _{t-\tau _i(t)}^t\left[ e(s-\tau _i(s))^TH_ie(s-\tau _i(s))+f(e(s-\tau _i(s)))^TJ_if(e(s-\tau _i(s)))\right] \mathrm{d}s\nonumber \\&\quad \le \frac{\alpha }{1+\beta \bar{\tau }}\int _{t-\tau _i(t)}^t\mathbf {E}\{V(s,e_{s-\tau _i(s)},i)\}\mathrm{d}s. \end{aligned}$$
(32)

Substituting (30)–(32) into (27) derives that

$$\begin{aligned}&\frac{\mathrm{d}\mathbf {E}\{V(t,e_t,i)\}}{\mathrm{d}t}\\&\qquad < -(\beta +\lambda \alpha )\mathbf {E}\{V(t,e_t,i)\}+\beta \mathbf {E}\{V(t,e_{t-\tau _i(t)},i)\}\\&\qquad +\frac{\alpha }{1+\beta \bar{\tau }}\left[ \mathbf {E}\{V(t,e_{t-\tau _i(t)},i)\} +\beta \int _{t-\tau _i(t)}^t\mathbf {E}\{V(s,e_{s-\tau _i(s)},i)\}\mathrm{d}s\right] \\&\quad \le -(\beta +\lambda \alpha )\mathbf {E}\{V(t,e_t,i)\}+\beta \mathbf {E}\{V(t,e_{t-\tau _i(t)},i)\}\\&\qquad +\frac{\alpha }{1+\beta \bar{\tau }}\left[ \mathbf {E}\{V(t,e_{t-\tau _i(t)},i)\}+\beta \tau _i(t)\sup _{[t-2\bar{\tau },t]} \{\mathbf {E}\{V(s,e_{s-\tau _i(s)},i)\}\}\right] \\&\quad \le -(\beta +\lambda \alpha )\mathbf {E}\{V(t,e_t,i)\}+(\beta +\alpha )\sup _{[t-2\bar{\tau },t]}\{\mathbf {E}\{V(s,e_s,i)\}\}. \end{aligned}$$

Applying Lemma 5 to above inequality results in that

$$\begin{aligned} V(t,e_t,i)\le \sup _{[-2\bar{\tau },0]}\{\mathbf {E}\{V(s,e_s,i)\}\}e^{-\varrho t}, \end{aligned}$$

where \(\varrho \) is the unique positive solution of the following equation:

$$\begin{aligned} \varrho =\beta +\lambda \alpha -(\alpha +\beta )e^{2\varrho \bar{\tau }}. \end{aligned}$$

Therefore we arrive at the conclusion that

$$\begin{aligned} \mathbf {E}\{||e(t)||^2\}\le e^{-\varrho t}\mathbf {E}\{||\varphi (t)||^2\}. \end{aligned}$$

The proof of Theorem 1 is completed. \(\square \)

Appendix II

1.1 Proof of Theorem 2

Consider the following Lyapunov-Krasovskii functional:

$$\begin{aligned} \widetilde{V}(t,{e_t},i)=V(t,{e_t},i)+\overline{V}(t,{e_t},i), \end{aligned}$$
(33)

where \(V(t,{e_t},i)\) ie defined in (15) and

$$\begin{aligned} \overline{V}(t,{e_t},i)&= \bar{\tau }_i\int _{t-\bar{\tau }_i}^t{\int _\nu ^t{\vartheta _i {{(s)}^T}{O_1}\vartheta _i (s)\mathrm{d}s\mathrm{d}\nu }}\\&+\bar{\upsilon }_i\int _{t-\bar{\upsilon }_i}^t{\int _\nu ^t{f{{(e(s))}^T}{O_2}f(e(s))\mathrm{d}s\mathrm{d}\nu }}+\int _{t-\bar{\tau }_i}^t{\int _\nu ^t{\rho _i {{(s)}^T}{O_3}\rho _i (s)\mathrm{d}s\mathrm{d}\nu }}. \end{aligned}$$

It can be easily verified that \(\widetilde{V}(t,e_t,i)\) is a nonnegative function over \([-\hat{\tau },+\infty ).\) Evaluating the weak infinitesimal operator of \(\widetilde{V}(t,e_t,i)\) along the trajectory of system (3), based on (7) we derive that

$$\begin{aligned} \pounds \overline{V}(t,{e_t},i)&= \bar{\tau }_i^2\vartheta _i {(t)^T}{O_1}\vartheta _i (t)-\bar{\tau }_i\bigg (1-\sum \limits _{j=1}^N{{{\pi }_{ij}}\bar{\tau }_j}\bigg )\int _{t-\bar{\tau }_i}^t{\vartheta _i {{(s)}^T}{O_1}\vartheta _i(s)\mathrm{d}s}\nonumber \\&+\bar{\upsilon }_if{{(e(t))}^T}{O_2}f(e(t))-\bar{\upsilon }_i\bigg (1-\sum \limits _{j=1}^N{{{\pi }_{ij}}{{{\bar{\upsilon }}}_j}}\bigg ) \int _{t-\bar{\upsilon }_i}^t{f{{(e(s))}^T}{O_2}f(e(s))\mathrm{d}s}\nonumber \\&+\bar{\tau }_i\rho _i {(t)^T}{O_3}\rho _i (t)-\int _{t-\bar{\tau }_i}^t{\rho _i {{(s)}^T}{O_3}\rho _i (s)\mathrm{d}s}\nonumber \\&= \bar{\tau }_i^2\vartheta _i{(t)^T}{O_1}\vartheta _i(t)-\bar{\tau }_i\bigg (1-\sum \limits _{j=1}^N{{{\pi }_{ij}}\bar{\tau }_j}\bigg ) \int _{t-\bar{\tau }_i}^t{\vartheta _i {{(s)}^T}{O_1}\vartheta _i (s)\mathrm{d}s}\nonumber \\&+\bar{\upsilon }_if{{(e(t))}^T}{O_2}f(e(t))-\bar{\upsilon }_i\bigg (1-\sum \limits _{j=1}^N{{{\pi }_{ij}}{{{\bar{\upsilon }}}_j}}\bigg ) \int _{t-\bar{\upsilon }_i}^t{f{{(e(s))}^T}{O_2}f(e(s))\mathrm{d}s}\nonumber \\&+\bar{\tau }_i\rho _i {(t)^T}{O_3}\rho _i (t)-\int _{t-\tau _i(t)}^t{\rho _i {{(s)}^T}{O_3}\rho _i(s)\mathrm{d}s}-\int _{t-\bar{\tau }_i}^{t-\tau _i(t)}{\rho _i {{(s)}^T}{O_3}\rho _i (s)\mathrm{d}s}.\nonumber \\ \end{aligned}$$
(34)

Furthermore, for any \(0<\tau _i(t)<\bar{\tau }_i,\ 0<\upsilon _i(t)<\bar{\upsilon }_i,\) from Lemma 2 we obtain that

$$\begin{aligned}&-\bar{\tau }_i\int _{t-\bar{\tau }_i}^t{\vartheta _i {{(s)}^T}{O_1}\vartheta _i (s)\mathrm{d}s}\nonumber \\&\quad = -\bar{\tau }_i\int _{t-\tau _i(t)}^t{\vartheta _i {{(s)}^T}{\bar{O}_1}\vartheta _i (s)\mathrm{d}s}-\bar{\tau }_i\int _{t-\bar{\tau }_i}^{t-\tau _i(t)}{\vartheta _i {{(s)}^T}{\bar{O}_1}\vartheta _i (s)\mathrm{d}s}\nonumber \\&\quad \le -\frac{\bar{\tau }_i}{\tau _i(t)}{{\left( \int _{t-\tau _i(t)}^t{\vartheta _i (s)ds} \right) }^T}{\bar{O}_1}\left( \int _{t-\tau _i(t)}^t{\vartheta _i (s)\mathrm{d}s} \right) \nonumber \\&\qquad -\frac{\bar{\tau }_i}{\bar{\tau }_i-\tau _i(t)}{{\left( \int _{t-\bar{\tau }_i}^{t-\tau _i(t)}{\vartheta _i (s)\mathrm{d}s} \right) }^T}{\bar{O}_1}\left( \int _{t-\bar{\tau }_i}^{t-\tau _i(t)}{\vartheta _i (s)\mathrm{d}s} \right) ,\end{aligned}$$
(35)
$$\begin{aligned}&-\bar{\upsilon }_i\int _{t-\bar{\upsilon }_i}^t{f{{(e(s))}^T}{\bar{O}_2}f(e(s))\mathrm{d}s}\nonumber \\&\quad = -\bar{\upsilon }_i\int _{t-\upsilon _i(t)}^t{f{{(e(s))}^T} {\bar{O}_2}f(e(s))\mathrm{d}s}-\bar{\upsilon }_i\int _{t-\bar{\upsilon }_i}^{t-\upsilon _i(t)}{f{{(e(s))}^T}{\bar{O}_2}f(e(s))\mathrm{d}s}\nonumber \\&\quad \le -\frac{\bar{\upsilon }_i}{\upsilon _i(t)}{{\left( \int _{t-\upsilon _i(t)}^t{f(e(s))\mathrm{d}s} \right) }^T}{\bar{O}_2}\left( \int _{t-\upsilon _i(t)}^t{f(e(s))\mathrm{d}s} \right) \nonumber \\&\qquad -\frac{\bar{\upsilon }_i}{\bar{\upsilon }_i-\upsilon _i(t)}{{\left( \int _{t-\bar{\upsilon }_i}^{t-\upsilon _i(t)}{f(e(s))\mathrm{d}s} \right) }^T}{\bar{O}_2}\left( \int _{t-\bar{\upsilon }_i}^{t-\upsilon _i(t)}{f(e(s))\mathrm{d}s} \right) . \end{aligned}$$
(36)

Denoting

$$\begin{aligned} \Xi _1=\left[ \int ^t_{t-\tau _i(t)}\vartheta _i(s)\mathrm{d}s\right] ^T\bar{O}_1\left[ \int ^t_{t-\tau _i(t)}\vartheta _i(s)\mathrm{d}s\right] , \end{aligned}$$
$$\begin{aligned}&\displaystyle \Xi _2=\left[ \int ^{t-\tau _i(t)}_{t-\bar{\tau }_i}\vartheta _i(s)\mathrm{d}s\right] ^T\bar{O}_1\left[ \int ^{t-\tau _i(t)}_{t-\bar{\tau }_i}\vartheta _i(s)\mathrm{d}s\right] ,\\&\displaystyle \Xi _3=\left[ \int ^t_{t-\upsilon _i(t)}f(e(s))\mathrm{d}s\right] ^T\bar{O}_2\left[ \int ^t_{t-\upsilon _i(t)}f(e(s))\mathrm{d}s\right] ,\\&\displaystyle \Xi _4=\left[ \int ^{t-\upsilon _i(t)}_{t-\bar{\upsilon }_i}f(e(s))\mathrm{d}s\right] ^T\bar{O}_2\left[ \int ^{t-\upsilon _i(t)}_{t-\bar{\upsilon }_i}f(e(s))\mathrm{d}s\right] , \end{aligned}$$

then from Lemma 3 we have that

$$\begin{aligned}&-\bar{\tau }_i\int _{t-\bar{\tau }_i}^t{\vartheta _i{{(s)}^T}{O_1}\vartheta _i(s)\mathrm{d}s}-\bar{\upsilon }_i\int _{t-\bar{\upsilon }_i}^t{f{{(e(s))}^T}{\bar{O}_2}f(e(s))\mathrm{d}s}\nonumber \\&\quad \le \max \big \{ -\Xi _1-3\Xi _2-\Xi _3-3\Xi _4,-\Xi _1-3\Xi _2-3\Xi _3-\Xi _4,\nonumber \\&\qquad -3\Xi _1-\Xi _2-\Xi _3-3\Xi _4, -3\Xi _1-\Xi _2-3\Xi _3-\Xi _4 \big \}. \end{aligned}$$
(37)

It is easy to verify that inequality (37) holds for any \(t>0\) with \(0\le \tau _i(t)\le \bar{\tau }_i,\ 0\le \upsilon _i(t)\le \bar{\upsilon }_i.\)

From [3], we obtain that

$$\begin{aligned}&\mathbf {E} \int _{t-\tau _i(t)}^t{\rho _i {{(s)}^T}{O_3}\rho _i (s)\mathrm{d}s}\nonumber \\&\quad =\mathbf {E} {{\left( \int _{t-\tau _i(t)}^t{\rho _i (s)\mathrm{d}\omega (s)} \right) }^T}{O_3}\left( \int _{t-\tau _i(t)}^t{\rho _i (s)\mathrm{d}\omega (s)} \right) ,\end{aligned}$$
(38)
$$\begin{aligned}&\mathbf {E} {{\left( \int _{t-\bar{\tau }_i}^{t-\tau _i(t)}{\rho _i {{(s)}^T}{O_3}\rho _i (s)\mathrm{d}s} \right) }^T}\nonumber \\&\quad =\mathbf {E} {{\left( \int _{t-\bar{\tau }_i}^{t-\tau _i(t)}{\rho _i (s)\mathrm{d}\omega (s)} \right) }^T}{O_3}\left( \int _{t-\bar{\tau }_i}^{t-\tau _i(t)}{\rho _i (s)\mathrm{d}\omega (s)} \right) . \end{aligned}$$
(39)

On the other hand, by the Leibniz-Newton formula, we get that

$$\begin{aligned} \int _{t-\tau _i(t)}^t{\vartheta _i(s)\mathrm{d}s}=e(t)-e(t-\tau _i(t))-\int _{t-\tau _i(t)}^t{\rho _i (s)\mathrm{d}\omega (s)}, \end{aligned}$$
(40)
$$\begin{aligned} \int _{t-\bar{\tau }_i}^{t-\tau _i(t)}{\vartheta _i(s)\mathrm{d}s}=e(t-\tau _i(t))-e(t-\bar{\tau }_i)-\int _{t-\bar{\tau }_i}^{t-\tau _i(t)}{\rho _i (s)\mathrm{d}\omega (s)}. \end{aligned}$$
(41)

Similar to the proof of Theorem 1, from (17)–(23), (34)–(41) and Lemma 1 we have that

$$\begin{aligned}&\mathbf {E}\pounds \widetilde{V}(t,{e_t},i)\\&\quad \le \xi _i{(t)^T}\left( \Omega _i+{{\mathbf {A}}_i}G_i(t)\mathbf {C}_i^T+{{\mathbf {C}}_i}G_i(t)\mathbf {A}_i^T\right) \xi _i(t) +\bar{\tau }_i^2\vartheta _i{(t)^T}{O_1}\vartheta _i(t)\\&\qquad +{{(D_ie(t)+{{E}_i}e(t-\tau _i(t)))}^T}\widehat{P}_iF_i{{(\varepsilon _iI -F_i^T\widehat{P}_iF_i)}^{-1}}F_i^T\widehat{P}_i(D_ie(t)\\&\qquad +{{E}_i}e(t-\tau _i(t)))+2\max \left\{ -\Xi _1-\Xi _3,-\Xi _1-\Xi _4,-\Xi _2-\Xi _3,-\Xi _2-\Xi _4 \right\} \\&\quad \le \xi _i{(t)^T}\left( \Omega _i+{{\epsilon }_i}{{\mathbf {C}}_i}G_i{(t)^T}G_i(t)\mathbf {C}_i^T +\epsilon _i^{-1}{{\mathbf {A}}_i}{{\mathbf {A}}_i}^T+{{\mathbf {B}}_i}\widehat{P}_iF_i{{(\varepsilon _iI -F_i^T\widehat{P}_iF_i)}^{-1}}\right. \\&\qquad \left. \times \, F_i^T\widehat{P}_i\mathbf {B}_i^T\right) \xi _i(t)+\bar{\tau }_i^2{{\left[ {\chi _{1i}}(t)+F_iG_i(t){\chi _{2i}}(t) \right] }^T}{O_1}\left[ {\chi _{1i}}(t)+F_iG_i(t){\chi _{2i}}(t) \right] \\&\qquad +\,2\max \left\{ -\Xi _1-\Xi _3,-\Xi _1-\Xi _4,-\Xi _2-\Xi _3,-\Xi _2-\Xi _4 \right\} , \end{aligned}$$

where

$$\begin{aligned} \xi _i(t)&= \hbox { col}\left\{ {\zeta _i}(t),\int _{t-\upsilon _i(t)}^t{f(e(s))ds},e(t-\bar{\tau }_i),\right. \\&\qquad \qquad \left. \int _{t-\bar{\upsilon }_i}^{t-\upsilon _i(t)}{f(e(s))\mathrm{d}s},\int _{t-\tau _i(t)}^t{\rho _i (s)\mathrm{d}\omega (s),}\int _{t-\bar{\tau }_i}^{t-\tau _i(t)}{\rho _i (s)\mathrm{d}\omega (s)} \right\} ,\\ {\chi _{1i}}(t)&= -(C_i-{X_i})e(t)+{Y_i}e(t-\tau _i(t))+A_if(e(t))\\&+B_if(e(t-\tau _i(t)))+{L_i} \int _{t-\upsilon _i(t)}^t{f(e(s))\mathrm{d}s}, \end{aligned}$$
$$\begin{aligned} {\chi _{2i}}(t)=-{K_{1i}}e(t)+{K_{2i}}f(e(t))+{K_{3i}}f(e(t-\tau _i(t)))+{K_{4i}}\int _{t-\upsilon _i(t)}^t{f(e(s))\mathrm{d}s}. \end{aligned}$$

Moreover, it follows from Assumption 2 and Lemma 1 that

$$\begin{aligned}&{{\left[ {\chi _{1i}}(t)+F_iG_i(t){\chi _{2i}}(t) \right] }^T}{O_1}\left[ {\chi _{1i}}(t)+F_iG_i(t){\chi _{2i}}(t) \right] \le {\chi _{1i}}{(t)^T}{O_1}{\chi _{1i}}(t)\\&\quad +{\chi _{1i}}{(t)^T}{O_1}F_i{{\left( \iota _iI -F_i^T{O_1}F_i\right) }^{-1}}F_i^T{O_1}{\chi _{1i}}(t)+\iota _i{\chi _{2i}}{(t)^T}{\chi _{2i}}(t). \end{aligned}$$

Therefore, we derive that

$$\begin{aligned} \mathbf {E}\pounds \widetilde{V}(t,{e_t},i)\le \xi _i{(t)^T}\widetilde{\Omega }_i\xi _i(t), \end{aligned}$$

where

$$\begin{aligned} \widetilde{\Omega }_i&= \Omega _i +{{\epsilon }_i}{{\mathbf {C}}_i}\mathbf {C}_i^T+\epsilon _i^{-1}{{\mathbf {A}}_i}^T{{\mathbf {A}}_i} +{{\mathbf {B}}_i}\widehat{P}_iF_i{{\left( \varepsilon _iI-F_i^T\widehat{P}_iF_i\right) }^{-1}}F_i^T\widehat{P}_i\mathbf {B}_i^T\\&+\bar{\tau }_i^2\left[ \mathbf {D}_i{O_1}\mathbf {D}_i^T+\mathbf {D}_i{O_1}F_i{{\left( \iota _iI -F_i^T{O_1}F_i\right) }^{-1}}F_i^T{O_1}\mathbf {D}_i^T+\iota _i\mathbf {C}_i\mathbf {C}_i^T\right] \\&+2\max \left\{ -\widehat{\Xi }_1-\widehat{\Xi }_3,-\widehat{\Xi }_1-\widehat{\Xi }_4,-\widehat{\Xi }_2-\widehat{\Xi }_3,-\widehat{\Xi }_2-\widehat{\Xi }_4 \right\} , \end{aligned}$$

with

$$\begin{aligned} \widehat{\Xi }_1= \widehat{\mathcal {I}}_1^T{\bar{O}_1}{\widehat{\mathcal {I}}_1},\ \ \widehat{\Xi }_2=\widehat{\mathcal {I}}_2^T\bar{O}_1\widehat{\mathcal {I}}_2,\ \ \widehat{\Xi }_3=\widehat{\mathcal {I}}_3^T\bar{O}_2\widehat{\mathcal {I}}_3,\ \ \widehat{\Xi }_4=\widehat{\mathcal {I}}_4^T\bar{O}_2\widehat{\mathcal {I}}_4, \end{aligned}$$
$$\begin{aligned} \widehat{\mathcal {I}}_1= \hbox { col}\left\{ -{I_n},\ {0_n},\ {I_n},\ {0_{4n\times n}},\ {I_n},\ 0_{n} \right\} , \end{aligned}$$
$$\begin{aligned} \widehat{\mathcal {I}}_2= \hbox { col}\left\{ 0_{2n\times n},\ -{I_n},\ {0_{2n\times n}},\ {I_n},\ {0_{2n\times n}},\ {I_n} \right\} , \end{aligned}$$
$$\begin{aligned} \widehat{\mathcal {I}}_3= \hbox { col}\left\{ 0_{7n\times n},\ {I_n},\ {0_{ n}} \right\} , \widehat{\mathcal {I}}_4=\hbox { col}\left\{ 0_{8n\times n},\ {I_n}\right\} . \end{aligned}$$

From the well-known Schur complement, it is easy to see that \(\mathbf {E}\pounds \widetilde{V}(t,{e_t},i)<0\) if and only if the LMIs (14) hold.

Setting \(\varsigma =\min _{i\in \mathcal {N}}\big \{\lambda _m\big (-\widetilde{\Omega }_i\big )\big \},\) it follows that \(\varsigma >0.\) For any \(t>0,\) we achieve that

$$\begin{aligned} \mathbf {E}\pounds \widetilde{V}(t,{e_t},i)\le -\varsigma \xi _i(t)^T\xi _i(t)\le -\varsigma e(t)^Te(t). \end{aligned}$$

By Dynkin’s formula, the following inequality holds

$$\begin{aligned} \mathbf {E}\widetilde{V}(t,e_t,\eta (t))-\mathbf {E}\widetilde{V}(0,e_0,\eta _0)\le -\varsigma \mathbf {E}\left\{ \int ^t_0e(s)^Te(s)\mathrm{d}s\right\} , \end{aligned}$$

and hence

$$\begin{aligned} \mathbf {E}\left\{ \int ^t_0e(s)^Te(s)\mathrm{d}s\right\} \le \frac{1}{\varsigma }\mathbf {E}\widetilde{V}(0,e_0,\eta _0), \end{aligned}$$

which implies that (3) is globally asymptotically stable in the mean square. This completes the proof of Theorem 2.

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Zheng, CD., Liang, W. & Wang, Z. Anti-Synchronization of Markovian Jumping Stochastic Chaotic Neural Networks with Mixed Time Delays. Circuits Syst Signal Process 33, 2761–2792 (2014). https://doi.org/10.1007/s00034-014-9773-x

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