Skip to main content
Log in

Exponential synchronization of stochastic chaotic neural networks with mixed time delays and Markovian switching

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

This paper studies the exponential synchronization problem for a class of stochastic perturbed chaotic neural networks with both Markovian jump parameters and mixed time delays. The mixed delays consist of discrete and distributed time-varying delays. At first, based on a Halanay-type inequality for stochastic differential equations, by virtue of drive-response concept and time-delay feedback control techniques, a delay-dependent sufficient condition is proposed to guarantee the exponential synchronization of two identical Markovian jumping chaotic-delayed neural networks with stochastic perturbation. Then, by utilizing the Jensen integral inequality and a novel Lemma, another delay-dependent criterion is established to achieve the globally stochastic robust synchronization. With some parameters being fixed in advance, these conditions can be solved numerically by employing the Matlab software. Finally, a numerical example with their simulations is provided to illustrate the effectiveness of the presented synchronization scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Arnold L (1972) Stochastic differential equations: theory and applications. Wiley, New York

    Google Scholar 

  2. Baker CTH, Buckwar E (2005) 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

    Article  MATH  MathSciNet  Google Scholar 

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

    MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  5. Gu K (2000) An integral inequality in the stability problem of time-delay systems. In: Proceedings of 39th IEEE conference on decision and control, Sydney, Australia, pp 2805–2810

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

    Article  MATH  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  8. Liu Z, Zhang H, Zhang Q (2010) 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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  11. Qian W, Li T, Cong S, Fei S (2010) Improved stability analysis on delayed neural networks with linear fractional uncertainties. Appl Math Comput 217:3596–3606

    Article  MATH  MathSciNet  Google Scholar 

  12. Sanchez EN, Perez JP (1999) Input-to-state stability (ISS) analysis for dynamic NN. IEEE Trans Circuits Syst I 46:1395–1398

    Article  MATH  MathSciNet  Google Scholar 

  13. Song Q (2010) Synchronization analysis in an array of asymmetric neural networks with time-varying delays and nonlinear coupling. Appl Math Comput 216:1605–1613

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  15. Song Q, Cao J (2011) Synchronization of nonidentical chaotic neural networks with leakage delay and mixed time-varying delays. Adv Differ Equ 16

    Article  MathSciNet  Google Scholar 

  16. De Souza CE, Li X (1999) Delay-dependent robust control of uncertain linear state-delayed systems. Automatica 35:1313–1321

    Article  MATH  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  18. Wang Z, Zhang H, Jiang B (2011) 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

    Article  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  20. Yue D, Tian E, Zhang Y, Peng C (2009) Delay-distribution-dependent stability and stabilization of T-S fuzzy systems with probabilistic interval delay. IEEE Trans Syst Man Cybern Part B 39:503–516

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  26. Zheng CD, Shan QH, Zhang H, Wang Z (2013) On stabilization of stochastic Cohen-Grossberg neural networks with mode-dependent mixed time-delays and markovian switching. IEEE Trans Neural Netw Learn Syst 24(5):800–811

    Article  Google Scholar 

  27. Zheng CD, Zhou F, Wang Z (2012) Stochastic exponential synchronization of jumping chaotic neural networks with mixed delays. Commun Nonlinear Sci Numer Simulat 17(3):1273–1291

    Article  MATH  MathSciNet  Google Scholar 

  28. Zhou W, Tong D, Gao Y, Ji C, Su H (2012) 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

    Article  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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cheng-De Zheng.

Appendices

Appendix 1

1.1 Proof of Theorem 1

Consider the following Lyapunov-Krasovskii functional:

$$ \begin{aligned} V_1(t,e_t,i)&=e(t)^TP_ie(t)+2\sum^N_{j=1}q_{ji}\int\limits^{e_j(t)}_0\left[\beta_j(s)-\lambda_js\right]\hbox{d}s+ 2\sum^n_{j=1}r_{ji}\int\limits^{e_j(t)}_0\left[\sigma_j s-f_j(s)\right]\hbox{d}s\\ &\quad +2\sum^n_{j=1}s_{ji}\int\limits^{e_j(t)}_0\left[f_j(s)-\gamma_js\right]\hbox{d}s+\int\limits^t_{t-\tau_i(t)}\left[e(s)^TUe(s)+f(e(s))^TWf(e(s)) \right]\hbox{d}s, \end{aligned} $$
(16)

where \(Q_i=\hbox{diag}\{q_{1i},q_{2i},\ldots,q_{ni}\}, R_i=\hbox{diag}\{r_{1i},r_{2i},\ldots,r_{ni}\}, S_i=\hbox{diag}\{s_{1i},s_{2i},\ldots,s_{ni}\}\).

It can be easily verified that V 1(t, e t , i) is a nonnegative function over \([-\bar{\tau},+\infty)\). Evaluating the time derivative of V 1(t, e t , i) along the trajectory of system (9), we have that

$$ \hbox{d}V_1(t,e_t,i)=\pounds V_1(t,e_t,i)\hbox{d}t+\frac{\partial}{\partial e}V_{1}(t,e_t,i)\rho_i(t)\hbox{d}\omega(t), $$
(17)

where

$$ \begin{aligned} \pounds V_1(t,e_t,i)&= 2\left\{ {e(t)^TP_i+[\beta (t)-\Uplambda e(t)]^T}{Q_i}+{[\Upsigma e(t)-f(e(t))]^T}{R_i}+{[f(e(t))-\Upgamma e(t)]^T}{S_i}\right\} \\ &\quad\times\left[-\beta (t)+{A_i}(t)f(e(t))+{B_i}(t)f(e(t-\tau_i(t))) +{Y_{1i}}e(t)+{Y_{2i}}e(t- \tau_i(t))\right]\\ &\quad+2\sum\limits_{k = 1}^N {{\pi_{ik}}\sum\limits_{j = 1}^n \int\limits_0^{{e_j}(t)}{\left\{ {q_{jk}} {[{\beta_j}(s)-{\lambda_j}(s)] + {r_{jk}} {[\sigma_js-f_j(s)]} +{s_{jk}} {[{f_j}(s)-{\gamma_j}(s)]} } \right\}}\hbox{d}s} \\ &\quad+e{(t)^T}\left(\sum^N_{j=1}\pi_{ij}P_j+U\right) e(t)-(1-\dot{\tau}_i(t)) e{(t-\tau_i(t))^T}Ue(t-\tau_i(t))+f{(e(t))^T}Wf(e(t))\\ &\quad+\sum\limits_{j = 1}^N {{\pi_{ij}}{\tau_j}(t)} \left[e{(t-\tau_i(t))^T}Ue(t-\tau_i(t))+f{(e(t-\tau_i(t)))^T}Wf(e(t- \tau_i(t)))\right]\\ &\quad-(1-\dot{\tau}_i(t))f{(e(t-\tau_i(t)))^T} Wf(e(t-\tau_i(t)))+\frac{1}{2}\hbox{trace}\left[ {\rho_i {{(t)}^T}\frac{{{\partial ^2}}}{{\partial {e^2}}}{V_1}(t,{e_t},i)\rho_i (t)} \right]. \end{aligned} $$
(18)

From Assumptions 3 and 4, we get that

$$ \begin{aligned} &2\sum\limits_{k = 1}^N {{\pi_{ik}} \sum\limits_{j = 1}^n \int\limits_0^{{e_j}(t)} {\left\{ {q_{jk}} {[{\beta_j}(s)-{\lambda_j}(s)] + {r_{jk}} {[\sigma_js-f_j(s)]} +{s_{jk}} {[{f_j} (s)-{\gamma_j}(s)]} } \right\}}\hbox{d}s } \\ &\le 2\sum\limits_{k = 1}^N {\pi^{\prime}_{ik}} \sum\limits_{j = 1}^n \int\limits_0^{{e_j}(t)} {\left\{ {q_{jk}} {({\delta_j}-{\lambda_j})s + {r_{jk}} ({\sigma_j}-{\gamma_j})s +{s_{jk}} ({\sigma_j}-{\gamma_j})s} \right\}\hbox{d}s } \\ &= e(t)^T\sum\limits_{k = 1}^N {\pi^{\prime}_{jk}} [{Q_k}(\Updelta-\Uplambda )+({R_k}+{S_k})(\Upsigma -\Upgamma )]e(t). \end{aligned} $$
(19)

In addition, we derive that

$$ \begin{aligned} &\sum\limits_{j = 1}^N {{\pi_{ij}}{\tau_j}(t)} \left[e{(t-\tau_i(t))^T}Ue(t-\tau_i(t))+f{(e(t-\tau_i(t)))^T}Wf(e(t- \tau_i(t)))\right]\\ & \le {\zeta_i}{(t)^T}\sum\limits_{j = 1}^N \pi^{\prime}_{ij} {\bar{\tau }_j}\left(\varsigma_4^TU\varsigma_4+ \varsigma_5^TU\varsigma_5\right){\zeta_i}(t). \end{aligned} $$
(20)

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

$$ \begin{aligned} &0\leq\frac{\hbox{d}(\beta_j(e_j)-\lambda_je_j)}{\hbox{d} e_j}\leq \delta_j-\lambda_j,\\ &0\leq\frac{\hbox{d}(f_j(e_j)-\gamma_je_j)}{\hbox{d} e_j}\leq \sigma_j-\gamma_j,\\ &0\leq\frac{\hbox{d}(\sigma_je_j-f_j(e_j))}{\hbox{d} e_j}\leq \sigma_j-\gamma_j. \end{aligned} $$

Thus, we have that

$$ \begin{aligned} \frac{1}{2}\frac{{{\partial ^2}}}{{\partial {e^2}}}{V_1}(t,{e_t},i)&=P_i+Q_i\times\hbox{diag}\left\{\frac{\hbox{d}(\beta_1(e_1)-\lambda_1e_1)}{\hbox{d} e_1},\ldots,\frac{{\hbox{d}}(\beta_n(e_n)-\lambda_ne_n)}{{\hbox{d}} e_n}\right\}\\ &\quad+R_i\times\hbox{diag}\left\{\frac{{\hbox{d}}(\sigma_1e_1-f_1(e_1))}{{\hbox{d}} e_1},\ldots,\frac{{\hbox{d}}(\sigma_ne_n-f_n(e_n))}{{\hbox{d}} e_n}\right\}\\ &\quad+S_i\times\hbox{diag}\left\{\frac{{\hbox{d}}(f_1(e_1)-\gamma_1e_1)}{\hbox{d} e_1},\ldots,\frac{{\hbox{d}}(f_n(e_n)-\gamma_ne_n)}{{\hbox{d}} e_n}\right\}\\ &\leq\bar{P}_i. \end{aligned} $$
(21)

For any \(j=1,2,\ldots,n,\) from (5) we obtain that

$$ \begin{aligned} &\left(f_j(e_j(t))- \sigma_je_j(t)\right)\left(f_j(e_j(t))-\gamma_je_j(t)\right)\leq 0,\\ &\left(f_j(e_j(t-\tau(t)))- \sigma_je_j(t-\tau(t))\right)\left(f_j(e_j(t-\tau(t))) -\gamma_je_j(t-\tau(t))\right)\leq 0. \end{aligned} $$

Therefore, the following matrix inequalities hold for any positive diagonal matrices J i , Z i with compatible dimensions

$$ 0\leq -e(t)^T\Upsigma\Upgamma J_ie(t)+e(t)^TJ_i(\Upsigma+\Upgamma)f(e(t))-f(e(t))^TJ_if(e(t)), $$
(22)
$$ \begin{aligned} &0\leq -e(t-\tau(t))^T\Upsigma\Upgamma Z_i e(t-\tau_i(t))\\ &\quad+e(t-\tau_i(t))^TZ_i(\Upsigma+\Upgamma) f(e(t-\tau_i(t)))-f(e(t-\tau_i(t)))^TZ_if(e(t-\tau_i(t))). \end{aligned} $$
(23)

According to Assumption 1 and Lemma 1, for any positive scalar \(\varepsilon_i\) we have that

$$ \begin{aligned} &\frac{1}{2}\hbox{trace}\left[ {\rho_i {{(t)}^T}\frac{{{\partial ^2}}}{{\partial {e^2}}}{V_1}(t,{e_t},i)\rho_i (t)} \right]\\ &=\left[C_ie(t)+D_ie(t-\tau_i(t))+E_i\Upphi_i(t)(H_{3i}e(t) +H_{4i}e(t-\tau_i(t)))\right]^T\\ &\quad\times\bar{P_i}\left[C_ie(t)+D_ie(t-\tau_i(t)) +E_i\Upphi_i(t)(H_{3i}e(t)+H_{4i}e(t-\tau_i(t)))\right]\\ &\leq\varepsilon_i^{-1}(H_{3i}e(t)+H_{4i}e(t-\tau_i(t)))^T (H_{3i}e(t)+H_{4i}e(t-\tau_i(t)))\\ &\quad+(C_ie(t)+D_ie(t-\tau_i(t)))^T\left(\bar{P_i}^{-1}-\varepsilon_i E_iE_i^T\right)^{-1}(C_ie(t)+D_ie(t-\tau_i(t))). \end{aligned} $$
(24)

From (5), the following inequalities hold for any positive diagonal matrix M i with compatible dimension

$$ 0\leq2\{e(t)^TM_i\beta(e(t))-e(t)^TM_i\Upgamma e(t)\}. $$
(25)

From (18–25), we obtain that

$$ \pounds {V_1}(t,{e_t},i)\leq \xi_i(t)^T\bar{\Upomega}_i(t)\xi_i(t)+ e(t-\tau_i(t))^TK_ie(t-\tau_i(t))+ f(e(t-\tau_i(t)))^TL_if(e(t-\tau_i(t))). $$
(26)

where

$$ \bar{\Upomega}_i(t)=\left[\begin{array}{ccccc} \psi_{1i}+\psi_{7i}&\psi_{2i}&\psi_{3i}+ \widetilde{P}_iA_i(t)&\widetilde{P}_iY_{2i} +\psi_{8i}&\widetilde{P}_iB_i(t)\\ *&-2Q_i&R_i-S_i+Q_iA_i(t)&Q_iY_{2i}&Q_iB_i(t)\\ *&*&W-J_i+\hbox{sym}\left((S_i-R_i)A_i(t)\right)&(S_i-R_i)Y_{2i}&(S_i-R_i)B_i(t)\\ *&*&*&\psi_{5i}+\psi_{9i}&\frac{1}{2}Z_i(\Upsigma+\Upgamma)\\ *&*&*&*&\psi_{6i}\\ \end{array}\right], $$

with

$$ \begin{aligned} \psi_{7i}&=C^T_i\left(\bar{P}^{-1}_i-\varepsilon_i E_iE^T_i\right)^{-1}C_i+\varepsilon^{-1}_i H^T_{3i}H_{3i},\\ \psi_{8i}&=C^T_i\left(\bar{P}^{-1}_i-\varepsilon_i E_iE^T_i\right)^{-1}D_i+\varepsilon^{-1}_i H^T_{3i}H_{4i},\\ \psi_{9i}&=D^T_i\left(\bar{P}^{-1}_i-\varepsilon_i E_iE^T_i\right)^{-1}D_i+\varepsilon^{-1}_i H^T_{4i}H_{4i}. \end{aligned} $$

Now, by (14), it is easy to see that there exists a scalar α > 1 such that

$$ \left[\begin{array}{ccccc} \tilde{\Uppsi}_i&{\mathcal{A}}_iE_i&{\mathcal{B}}_i&{\mathcal{C}}_i&0\\ *&-\epsilon_i I&0&0&0\\ *&*&-\varepsilon_i F_iF^T_i&0&I\\ *&*&*& -\varepsilon_i I&0\\ *&*&*&*&-\bar{P_i}\end{array}\right]<0, $$
(27)

where

$$ \tilde{\Uppsi}_i=\left[\begin{array}{ccccc} \alpha F_i+\psi_{1i}&\psi_{2i}&\psi_{3i}+\widetilde{P}_iA_i& \widetilde{P}_iY_{2i}+\psi_{8i}&\widetilde{P}_iB_i\\ *&-2Q_i&R_i-S_i+Q_iA_i&Q_iY_{2i}&Q_iB_i\\ *&*&W-J_i+\hbox{sym}((S_i-R_i)A_i)&(S_i-R_i)Y_{2i}&(S_i-R_i)B_i\\ *&*&*&\psi_{5i}+\psi_{9i}&\frac{1}{2}Z_i(\Upsigma+\Upgamma)\\ *&*&*&*&\psi_{6i}\\ \end{array}\right]. $$

Applying Schur complements to (27) results in

$$ \begin{aligned} &{\left[\begin{array}{ccccc} \alpha F_i+\psi_{1i}+\psi_{7i}&\psi_{2i}&\psi_{3i}+ \widetilde{P}_iA_i & \widetilde{P}_iY_{2i}+\psi_{8i}&\widetilde{P}_iB_i\\ {*}&-2Q_i&R_i-S_i+Q_iA_i&Q_iY_{2i}&Q_iB_i\\ {*}&{*}&W-J_i+\hbox{sym}((S_i-R_i)A_i)&(S_i-R_i)Y_{2i}&(S_i-R_i)B_i\\ {*}&{*}&{*}&\psi_{5i}+\psi_{9i}&\frac{1}{2}Z_i(\Upsigma+\Upgamma)\\ {*}&{*}&{*}&{*}&\psi_{6i}\\ \end{array}\right]}\\ &\quad+{\epsilon^{-1}_i\left[\begin{array}{ccccc} \widetilde{P}_iE_i\\ Q_iE_i\\ (S_i-R_i)E_i\\ 0\\ 0\\ \end{array}\right]\left[\begin{array}{cccc} \widetilde{P}_iE_i\\ Q_iE_i\\ (S_i-R_i)E_i\\ 0\\ 0\\ \end{array}\right]^T+\epsilon_i\left[\begin{array}{ccccc} 0\\ 0\\ H_{1i}^T\\ 0\\ H_{2i}^T\\ \end{array}\right]\left[\begin{array}{ccccc} 0\\ 0\\ H_{1i}^T\\ 0\\ H_{2i}^T\\ \end{array}\right]^T<0}. \end{aligned} $$
(28)

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

$$ \begin{aligned} &{\left[\begin{array}{ccccc} 0&0&\widetilde{P}_i\Updelta A_i(t)&0&\widetilde{P}_i\Updelta B_i(t)\\ {*}&0&Q_i\Updelta A_i(t)&0&Q_i\Updelta B_i(t)\\ {*}&{*}&\hbox{sym}((S_i-R_i)\Updelta A_i(t))&0&(S_i-R_i) \Updelta B_i(t)\\ {*}&{*}&{*}&0&0\\ {*}&{*}&{*}&{*}&0\\ \end{array}\right]}\\ &{=\left[\begin{array}{ccccc} \widetilde{P}_iE_i\\ Q_iE_i\\ (S_i-R_i)E_i\\ 0\\ 0\\ \end{array}\right]\Upphi_i(t)\left[\begin{array}{ccccc} 0\\ 0\\ H_{1i}^T\\ 0\\ H_{2i}^T\\ \end{array}\right]^T+\left[\begin{array}{ccccc} 0\\ 0\\ H_{1i}^T\\ 0\\ H_{2i}^T\\ \end{array}\right]\Upphi_i(t)^T\left[\begin{array}{ccccc} \widetilde{P}_iE_i\\ Q_iE_i\\ (S_i-R_i)E_i\\ 0\\ 0\\ \end{array}\right]^T}\\ &{\leq\epsilon^{-1}_i\left[\begin{array}{ccccc} \widetilde{P}_iE_i\\ Q_iE_i\\ (S_i-R_i)E_i\\ 0\\ 0\\ \end{array}\right]\left[\begin{array}{ccccc} \widetilde{P}_iE_i\\ Q_iE_i\\ (S_i-R_i)E_i\\ 0\\ 0\\ \end{array}\right]^T+\epsilon_i\left[\begin{array}{ccccc} 0\\ 0\\ H_{1i}^T\\ 0\\ H_{2i}^T\\ \end{array}\right]\left[\begin{array}{ccccc} 0\\ 0\\ H_{1i}^T\\ 0\\ H_{2i}^T\\ \end{array}\right]^T.} \end{aligned} $$

This together with (28) provides that

$$ \bar{\Upomega}_i(t)+\hbox{diag}\begin{array}{ccccc} \{\alpha F_i& 0& 0& 0& 0\}<0 \end{array}. $$

By this inequality and (26), it is easy to see that

$$ \pounds V_1(t,e_t,i)<-\alpha e(t)^TF_ie(t)+e(t-\tau_i(t))^TK_ie(t-\tau_i(t)) +f(e(t-\tau_i(t)))^TL_if(e(t-\tau_i(t))). $$

Taking the mathematical expectations on both sides of (17), from above inequality we have that

$$ \begin{aligned} \hbox{d}{\mathbb{E}}\{V_1(t,e_t,i)\}&={\mathbb{E}}\pounds V_1(t,e_t,i)\hbox{d}t+{\mathbb{E}}\left\{\frac{\partial}{\partial e}V_1(t,e_t,i)\rho_i(t)\hbox{d}\omega(t)\right\}\\ &<\left[-\alpha e(t)^TF_ie(t)\hbox{d}t+e(t-\tau_i(t))^TK_ie(t-\tau_i(t))\hbox{d}t+f(e(t-\tau_i(t)))^TL_if(e(t-\tau_i(t)))\right]\hbox{d}t. \end{aligned} $$

By integrating above inequality from t − τ(t) to t, we obtain that

$$ \begin{aligned} {\mathbb{E}}\{V_1(t,e_t,i)\}-{\mathbb{E}} \{V_1(t,e_{t-\tau_i(t)},i)\}&=\int\limits_{t-\tau_i(t)}^t {\mathbb{E}}\{V_1(s,e_s,i)\}\hbox{d}s \\ &<-\alpha\int\limits_{t-\tau_i(t)}^t e(s)^TF_ie(s)\hbox{d}s+\int\limits_{t-\tau_i(t)}^t \left[e(s-\tau_i(s))^TK_ie(s-\tau_i(s))\right.\\ &\quad\left. +f(e(s-\tau_i(s)))^TL_if(e(s-\tau_i(s)))\right]\hbox{d}s. \end{aligned} $$

It follows that

$$ \begin{aligned} &{\mathbb{E}}\left[\frac{{\hbox{d}}V_1(t,e_t,i)}{\hbox{d}t} \right]+ \iota {\mathbb{E}}\left[V_1(t,e_t,i)-V_1(t,e_{t-\tau_i(t)},i)\right]\\ &\quad<-\alpha e(t)^TF_ie(t)+e(t-\tau_i(t))^TK_ie(t-\tau_i(t))\\ &+f(e(t-\tau_i(t)))^TL_if(e(t-\tau_i(t)))-\iota\alpha\int\limits_{t-\tau_i(t)}^t e(s)^TF_ie(s)\hbox{d}s\\ &+\iota\int\limits_{t-\tau(t)}^t \left[e(s-\tau_i(s))^TKe(s-\tau_i(s)) +f(e(s-\tau_i(s)))^TL_if(e(s-\tau_i(s)))\right]\hbox{d}s. \end{aligned} $$
(29)

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

$$ -e(t)^TF_ie(t)\leq -\nu e(t)^T\bar{P_i}e(t), $$
(30)
$$ -\iota\int\limits_{t-\tau_i(t)}^t e(s)^TF_ie(s)\hbox{d}s\leq -\nu\int\limits_{t-\tau_i(t)}^t \left[f(e(s))^TWf(e(s))+e(s)^TUe(s)\right]\hbox{d}s. $$
(31)

Noticing that

$$ \begin{aligned} 2\sum^n_{j=1}q_{ji}\int\limits^{e_j(t)}_0\left[\beta_j(s)-\lambda_js\right]\hbox{d}s&\leq 2\sum^n_{j=1}q_{ji}\int\limits^{e_j(t)}_0(\delta_j-\gamma_j)s\hbox{d}s=e(t)^TQ_i(\Updelta-\Uplambda)e(t),\\ 2\sum^n_{j=1}r_{ji}\int\limits^{e_j(t)}_0\left[\sigma_j s-f_j(s)\right]\hbox{d}s&\leq 2\sum^n_{j=1}r_{ji}\int\limits^{e_j(t)}_0(\sigma_j-\gamma_j)s\hbox{d}s=e(t)^TR_i(\Upsigma-\Upgamma)e(t),\\ 2\sum^n_{j=1}s_{ji}\int\limits^{e_j(t)}_0\left[f_j(s)-\gamma_js\right]\hbox{d}s&\leq 2\sum^n_{j=1}s_{ji}\int\limits^{e_j(t)}_0(\sigma_j-\gamma_j)s\hbox{d}s=e(t)^TS_i(\Upsigma-\Upgamma)e(t). \end{aligned} $$

Therefore, from (16) we have that

$$ {\mathbb{E}}\{V_1(t,e_t,i)\}\leq e(t)^T\bar{P_i}e(t)+\int\limits_{t-\tau_i(t)}^t \left[e(s)^TUe(s)+f(e(s))^TWf(e(s))\right]\hbox{d}s. $$

This together with (30–31) yields that

$$ -e(t)^TF_ie(t)-\iota\int\limits_{t-\tau_i(t)}^t \left[e(s)^TF_ie(s)\right]\hbox{d}s\leq-\nu {\mathbb{E}}\{V_1(t,e_t,i)\}. $$
(32)

Moreover, \({\mathbb{E}\{V_1(t,e_t,i)\}\geq e(t)^TP_ie(t)}\), therefore, it follows from (5) and (12) that

$$ \begin{aligned} &e(t-\tau_i(t))^TK_ie(t-\tau_i(t)) +f(e(t-\tau_i(t)))^TL_if(e(t-\tau_i(t)))\\ &\leq e(t-\tau_i(t))^T\left(K_i+\Uptheta L_i\Uptheta\right)e(t-\tau_i(t))\\ &\leq \frac{\nu}{1+\iota\bar{\tau}_i}e(t-\tau_i(t))^TP_ie(t-\tau_i(t))\\ &\leq \frac{\nu}{1+\iota\bar{\tau}_i}{\mathbb{E}}\{V_1(t,e_{t-\tau_i(t)},i)\}. \end{aligned} $$
(33)

Thus, we obtain that

$$ \begin{aligned} &\int\limits_{t-\tau_i(t)}^t\left[e(s-\tau_i(s))^TK_ie(s-\tau_i(s)) +f(e(s-\tau_i(s)))^TL_if(e(s-\tau_i(s)))\right]\hbox{d}s\\ &\leq \frac{\nu}{1+\iota\bar{\tau}_i} \int\limits_{t-\tau_i(t)}^t{\mathbb{E}}\{V_1(s,e_{s-\tau_i(s)},i)\}\hbox{d}s. \end{aligned} $$
(34)

Substituting (32–34) into (29) derives that

$$ \begin{aligned} &\frac{{\hbox{d}}{\mathbb{E}}\{V_1(t,e_t,i)\}}{\hbox{d}t}<-(\iota+\alpha\nu){\mathbb{E}}\{V_1(t,e_t,i)\}+\iota {\mathbb{E}}\{V_1(t,e_{t-\tau_i(t)},i)\} \\ &\quad+\frac{\nu}{1+\iota\bar{\tau}_i} \left[{\mathbb{E}}\{V_1(t,e_{t-\tau_i(t)},i)\}+\iota\int\limits_{t-\tau_i(t)}^t {\mathbb{E}}\{V_1(s,e_{s-\tau_i(s)})\}\hbox{d}s\right]\\ &\leq-(\iota+\alpha\nu){\mathbb{E}}\{V_1(t,e_t,i)\}+\iota {\mathbb{E}}\{V_1(t,e_{t-\tau_i(t)},i)\}\\ &\quad+\frac{\nu}{1+\iota\bar{\tau}_i}\left[{\mathbb{E}} \{V_1(t,e_{t-\tau_i(t)},i)\}+\iota\tau_i(t)\sup_{[t-2\bar{\tau},t]} {\mathbb{E}}\{V_1(s,e_{s-\tau_i(s)})\}\right]\\ &\leq-(\iota+\alpha\nu){\mathbb{E}}\{V_1(t,e_t,i)\} +(\iota+\nu)\sup_{[t-2\bar{\tau},t]}{\mathbb{E}}\{V_1(s,e_s,i)\}. \end{aligned} $$

Noting that α > 1, applying Lemma 5 to above inequality results in

$$ {\mathbb{E}}\{V_1(t,e_t,i)\}\leq \sup_{[-2\bar{\tau},0]}{\mathbb{E}}\{V_1(s,e_s,i)\}e^{-\kappa t}, $$

where κ is the unique positive solution of the following equation:

$$ \kappa =\iota+\alpha\nu-(\nu+\iota)e^{2\kappa \bar{\tau}}. $$

Therefore, we arrive at the conclusion that

$$ {\mathbb{E}}\{||e(t)||^2\}\leq e^{-\kappa t}{\mathbb{E}}\{||\varphi(t)||^2\}. $$

The proof is completed.

Appendix 2

1.1 Proof of Theorem 2

Define the following Lyapunov-Krasovskii functional:

$$ V(t,{e_t},i) = \sum\limits_{j = 1}^2 {{V_j}(t,{e_t},i)}, $$

where V 1(t, e t , i) ie defined in (16) and

$$ \begin{aligned} {V_2}(t,{e_t},i) &= {\bar{\tau }_i}\int\limits_{t-{\bar{\tau }_i}}^t {\int\limits_v^t {\chi_i {{(s)}^T}{T_1}\chi_i (s)\hbox{d}s\hbox{d}v} } +{\bar{\varrho}_i}\int\limits_{t- {\bar{\varrho}_i}}^t {\int\limits_v^t {f{{(e(s))}^T}{T_2}f(e(s))\hbox{d}s\hbox{d}v} } \\ &\quad+\int\limits_{t-{\bar{\tau }_i}}^t {\int\limits_v^t {\rho {{(s)}^T}{T_3}\rho (s)\hbox{d}s\hbox{d}v} }. \end{aligned} $$

It can be easily verified that V(t, e t , i) is a nonnegative function over \([-\hat{\tau},+\infty)\). Evaluating the time derivative of V(t, e t , i) along the trajectory of system (3), we have that

$$ \hbox{d}V(t,e_t,i)=\pounds V(t,e_t,i)\hbox{d}t+\frac{\partial}{\partial e}V(t,e_t,i)\rho_i(t)\hbox{d}\omega(t), $$
(35)

where

$$ \begin{aligned} \pounds V_1(t,e_t,i)&\leq 2\left\{ {e(t)^TP_i+[\beta (t)-\Uplambda e(t)]^T}{Q_i}+{[\Upsigma e(t)-f(e(t))]^T}{R_i}+{[f(e(t))-\Upgamma e(t)]^T}{S_i}\right\}\\ &\quad\times\left[-\beta (t)+{A_i}(t)f(e(t))+{B_i}(t)f(e(t-\tau_i(t)))+{G_i}(t)\int\limits_{t-{\varrho_i}(t)}^t {f(e(s))} \hbox{d}s \right.\\ &\left.\quad+{Y_{1i}}e(t)+{Y_{2i}}e(t- \tau_i(t))\right]+e(t)^T\sum\limits_{k = 1}^N {\pi^{\prime}_{jk}} [{Q_k}(\Updelta -\Uplambda )+({R_k}+{S_k})(\Upsigma -\Upgamma )]e(t)\\ &\quad+e{(t)^T}\left(\sum^N_{j=1}\pi_{ij}P_j+U\right)e(t)-(1-\dot{\tau}_i(t)) e{(t-\tau_i(t))^T}Ue(t-\tau_i(t)) \\ &\quad+\sum\limits_{j = 1}^N \pi^{\prime}_{ij} {\bar{\tau }_j} \left[e{(t-\tau_i(t))^T}Ue(t-\tau_i(t))+f{(e(t-\tau_i(t)))^T}Wf(e(t- \tau_i(t)))\right]\\ &\quad+f{(e(t))^T}Wf(e(t))- (1-\dot{\tau}_i(t))f{(e(t-\tau_i(t)))^T}Wf(e(t-\tau_i(t)))+\rho_i (t)^T\widetilde{P}_i\rho_i (t), \end{aligned} $$
(36)
$$ \begin{aligned} \pounds{V_2}(t,{e_t},i) &= \bar \tau_i^2\chi_i {(t)^T}{T_1}\chi_i (t)-{\bar{\tau }_i}\left( {1-\sum\limits_{j = 1}^N {{\pi_{ij}}{\bar{\tau }_j}} } \right)\int\limits_{t-{\bar{\tau }_i}}^t {\chi_i {{(s)}^T}{T_1}\chi_i (s)\hbox{d}s}\\ &\quad+\bar\varrho_i^2f{(e(t))^T}{T_2}f(e(t))-{{\bar\varrho}_i}\left( {1-\sum\limits_{j = 1}^N {{\pi_{ij}}{{\bar\varrho}_j}} } \right)\int\limits_{t-{{\bar\varrho}_i}}^t {f{{(e(s))}^T}{T_2}f(e(s))\hbox{d}s} \\ &\quad+{\bar{\tau }_i}\rho_i {(t)^T}{T_3}\rho_i (t)-\left( {1-\sum\limits_{j = 1}^N {{\pi_{ij}}{\bar{\tau }_j}} } \right)\int\limits_{t-\bar \tau_i }^t {\rho_i {{(s)}^T}{T_3}\rho_i (s)\hbox{d}s}. \end{aligned} $$
(37)

For any t with \(0<\tau_i(t)<\bar\tau_i\) and \(0<\varrho_i(t)<\bar\varrho_i\), from Lemma 2 we have the following inequalities

$$ \begin{aligned} &-{\bar{\tau }_i}\left( {1-\sum\limits_{j = 1}^N {{\pi_{ij}}{\bar{\tau }_j}} } \right)\int\limits_{t-{\bar{\tau }_i}}^t {\chi_i {{(s)}^T}{T_1}\chi_i (s)\hbox{d}s}\\ &\quad=- {\bar{\tau }_i}\int\limits_{t-\tau_i(t)}^t {\chi_i {{(s)}^T}{\bar{T}_1}\chi_i (s)\hbox{d}s} -{\bar{\tau }_i}\int\limits_{t-{\bar{\tau }_i}}^{t- \tau_i(t)} {\chi_i {{(s)}^T}{\bar{T}_1}\chi_i (s)\hbox{d}s}\\ &\quad\leq- \frac{\bar\tau_i}{\tau_i(t)}\left(\int\limits_{t-\tau_i(t)}^t \chi_i(s)\hbox{d}s\right)^T\bar{T}_1\left(\int\limits_{t-\tau_i(t)}^t \chi_i(s)\hbox{d}s\right)\\ &\quad-\frac{\bar\tau_i}{\bar\tau_i-\tau_i(t)} \left(\int\limits_{t-\bar\tau_i}^{t-\tau_i(t)} \chi_i(s)\hbox{d}s\right)^T\bar T_1\left(\int\limits_{t-\bar\tau_i}^{t-\tau_i(t)} \chi_i(s)\hbox{d}s\right), \end{aligned} $$
(38)
$$ \begin{aligned} &-{{\bar\varrho}_i}\left( {1-\sum\limits_{j = 1}^N {{\pi_{ij}}{{\bar\varrho}_j}} } \right)\int\limits_{t-{{\bar\varrho}_i}}^t {f{{(e(s))}^T}{T_2}f(e(s))\hbox{d}s} \\ &\quad= -{{\bar\varrho}_i}\int\limits_{t-{\varrho_i}(t)}^t {f{{(e(s))}^T}{\bar{T}_2}f(e(s))\hbox{d}s} -{{\bar\varrho}_i}\int\limits_{t-{{\bar\varrho}_i}}^{t-{\varrho_i}(t)} {f{{(e(s))}^T}{\bar{T}_2}f(e(s))\hbox{d}s} \\ &\quad\leq- \frac{\bar\varrho_i}{{\varrho_i}(t)}\left(\int\limits_{t-{\varrho_i}(t)}^t f(e(s))\hbox{d}s\right)^T\bar{T}_2\left(\int\limits_{t-{\varrho_i}(t)}^t f(e(s))\hbox{d}s\right) \\ &\quad- \frac{\bar\varrho_i}{\bar\varrho_i-{\varrho_i}(t)} \left(\int\limits_{t-\bar\varrho_i}^{t-\varrho_i(t)} f(e(s))\hbox{d}s\right)^T\bar{T}_2\left(\int\limits_{t-\bar\varrho_i}^{t-\varrho_i(t)} f(e(s))\hbox{d}s\right). \end{aligned} $$
(39)

Set λ j  = 1, μ j  = 3, based on Lemma 2 we get from (38–39) that

$$ \begin{aligned} &- {\bar{\tau }_i}\int\limits_{t-{\bar{\tau }_i}}^t {\chi_i{{(s)}^T}{\bar{T}_1}\chi_i(s)\hbox{d}s} -{{\bar\varrho}_i}\int\limits_{t-\bar\varrho_i}^{t} {f{{(e(s))}^T}{\bar{T}_2}f(e(s))\hbox{d}s} \\ &\quad\le \max \left\{-\varsigma_7^T{\bar{T}_1}{\varsigma_7}-3\varsigma_8^T {\bar{T}_1}{\varsigma_8}-\varsigma_6^T{\bar{T}_2}{\varsigma_6}- 3\varsigma_{11}^T{\bar{T}_2}{\varsigma_{11}}, -\varsigma_7^T{\bar{T}_1}{\varsigma_7}-3 \varsigma_8^T{\bar{T}_1}{\varsigma_8}-3\varsigma_6^T {\bar{T}_2}{\varsigma_6}- \varsigma_{11}^T{\bar{T}_2}{\varsigma_{11}},\right.\\ &\left.\quad\quad\quad\quad-3\varsigma_7^T{\bar{T}_1}{\varsigma_7} -\varsigma_8^T{\bar{T}_1}{\varsigma_8}-3 \varsigma_6^T{\bar{T}_2}{\varsigma_6}- \varsigma_{11}^T{\bar{T}_2}{\varsigma_{11}} ,-3\varsigma_7^T{\bar{T}_1}{\varsigma_7} -\varsigma_8^T{\bar{T}_1}{\varsigma_8}-\varsigma_6^T {\bar{T}_2}{\varsigma_6}- 3\varsigma_{11}^T{\bar{T}_2}{\varsigma_{11}}\right\}. \end{aligned} $$
(40)

It is easy to verify that Eq. (40) holds for any t with \(0\leq\tau_i(t)\leq\bar\tau_i\) and \(0\leq\varrho_i(t)\leq\bar\varrho_i.\)

From [4, 17], we have that

$$ {\mathbb{E}}\left(\int\limits_{t-\tau_i(t)}^t {\rho_i {{(s)}^T}{T_3}\rho_i (s)} \hbox{d}s\right) ={\mathbb{E}}\left\{\left( \int\limits_{t-\tau_i(t)}^t \rho_i (s)\hbox{d}\omega(s) \right)^TT_3\left( \int\limits_{t-\tau_i(t)}^t \rho_i (s)\hbox{d}\omega(s) \right)\right\}, $$
(41)
$$ {\mathbb{E}}\left(\int\limits_{t-{\bar{\tau }_i}}^{t-\tau_i(t)} {\rho_i {{(s)}^T}{T_3}\rho_i (s)} \hbox{d}s\right) = {\mathbb{E}}\left\{ {\left( {\int\limits_{t-{\bar{\tau }_i}}^{t-\tau_i(t)} {\rho_i (s)} \hbox{d}\omega(s)} \right)^T}{T_3}\left( {\int\limits_{t-{\bar{\tau }_i}}^{t-\tau_i(t)} {\rho_i (s)} \hbox{d}\omega(s)} \right)\right\}. $$
(42)

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

$$ \int\limits_{t-\tau_i(t)}^t {\chi_i(s)} \hbox{d}s = e(t)-e(t-\tau_i(t))-\int\limits_{t-\tau_i(t)}^t {\rho_i (s)} \hbox{d}\omega(s). $$

Therefore, the following equalities hold for any real matrices X ji (j = 1, 2, 3) with compatible dimensions

$$ \begin{aligned} &2\chi_i{(t)^T}{X^T_{1i}}\left\{\vphantom{\int\limits_{t-{\varrho_i}(t)}^t }-\chi_i(t)-\beta(e(t)) +{A_i}(t)f(e(t))+{B_i}(t)f(e(t-\tau_i(t)))\right.\\ &\left.\quad\quad\quad+{G_i}(t)\int\limits_{t-{\varrho_i}(t)}^t {f(e(s))} \hbox{d}s+{Y_{1i}}e(t)+{Y_{2i}}e(t-\tau_i(t))\right\} = 0, \end{aligned} $$
(43)
$$ 2\left({X_{2i}}e(t)+{X_{3i}}e(t-\tau_i(t))\right)^T\left\{e(t)-e(t-\tau_i(t))-\int\limits_{t-\tau_i(t)}^t {\rho_i (s)} \hbox{d}\omega(s)-\int\limits_{t-\tau_i(t)}^t {\chi_i(s)} \hbox{d}s\,\right\} = 0. $$
(44)

From Lemma 1, the following matrix inequalities hold for any positive scalar \(\epsilon_i\)

$$ \begin{aligned} &2{\zeta_i}{(t)^T}\psi_{ai}^T(\Updelta{A_i}(t){\varsigma_3} +\Updelta{B_i}(t){\varsigma_5}+ \Updelta{G_i}(t){\varsigma_6}){\zeta_i}(t)\\ &\quad= 2{\zeta_i}{(t)^T}\psi_{ai}^T{E_i}\Upphi_i(t)({H_{1i}}{\varsigma_3}+{H_{2i}}{\varsigma_5}+ {H_{3i}}{\varsigma_6}){\zeta_i}(t)\\ &\quad\le {\zeta_i}{(t)^T}\left\{ {\epsilon_i^{-1}\psi_{ai}^T{E_i}E_i^T\psi_{ai}^T}+{\epsilon_i} {{({H_{1i}}{\varsigma_3}+{H_{2i}}{\varsigma_5}+{H_{3i}}{\varsigma_6})}^T} ({H_{1i}}{\varsigma_3}+{H_{2i}}{\varsigma_5}+ {H_{3i}}{\varsigma_6}) \right\}{\zeta_i}(t). \end{aligned} $$
(45)

By (22–25), (36–37) and (40–45), and taking the mathematical expectations on both sides of (35), we obtain that

$$ \begin{aligned} \hbox{d}{\mathbb{E}}\{V(t,e_t,i)\}&={\mathbb{E}}\pounds V(t,e_t,i)\hbox{d}t+{\mathbb{E}}\left\{\frac{\partial}{\partial e}V(t,e_t,i)\rho_i(t)\hbox{d}\omega(t)\right\}\\ &\leq\zeta_i(t)^T\left(\widetilde{\Upomega}_i+2\max \left\{ -\varsigma_8^T{\bar{T}_1}{\varsigma_8}-\varsigma_{11}^T{\bar{T}_2} {\varsigma_{11}},-\varsigma_8^T{\bar{T}_1}{\varsigma_8}-\varsigma_6^T {\bar{T}_2}{\varsigma_6},\right.\right.\\ &\left.\left.\quad\quad-\varsigma_7^T{\bar{T}_1}{\varsigma_7}-\varsigma_6^T {\bar{T}_2}{\varsigma_6},-\varsigma_7^T{\bar{T}_1} {\varsigma_7}-\varsigma_{11}^T{\bar{T}_2}{\varsigma_{11}}\right\}\right) \zeta_i(t). \end{aligned} $$

From (15), there exists a positive scalar α 0 such that

$$ \hbox{d}{\mathbb{E}}\{V(t,e_t,i)\}<-\alpha_0{\mathbb{E}}||e(t)||^2. $$

Similar to the proof of Theorem 1 in [27], it implies that the error system (3) is globally exponentially stable. This completes the proof.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zheng, CD., Zhang, H. & Wang, Z. Exponential synchronization of stochastic chaotic neural networks with mixed time delays and Markovian switching. Neural Comput & Applic 25, 429–442 (2014). https://doi.org/10.1007/s00521-013-1507-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-013-1507-7

Keywords

Navigation