Mean-square delay-distribution-dependent exponential synchronization of chaotic neural networks with mixed random time-varying delays and restricted disturbances

Quan Hai; Shutang Liu

doi:10.3934/dcdsb.2020221

Article Contents

2021, Volume 26, Issue 6: 3097-3118. Doi: 10.3934/dcdsb.2020221

This issue Previous Article An almost periodic Dengue transmission model with age structure and time-delayed input of vector in a patchy environment Next Article A subgrid stabilizing postprocessed mixed finite element method for the time-dependent Navier-Stokes equations

Mean-square delay-distribution-dependent exponential synchronization of chaotic neural networks with mixed random time-varying delays and restricted disturbances

Quan Hai^1,2, and
Shutang Liu^1, ,

1.
College of Control Science and Engineering, Shandong University, Jinan 250061, China
2.
College of Mathematics Science Inner Mongolia Normal University Hohhot 010022, China

^* Corresponding author: stliu618@163.com
^* Corresponding author: stliu618@163.com

Received: November 30, 2019

Revised: March 31, 2020

Early access: July 2020

Published: June 2021

The first author is supported by NSF of china 61533011, U1806203

Abstract Full Text(HTML) Figure(4) Related Papers Cited by

Abstract

This paper investigates the delay-distribution-dependent exponential synchronization problem for a class of chaotic neural networks with mixed random time-varying delays as well as restricted disturbances. Given the probability distribution of the time-varying delay, stochastic variable that satisfying Bernoulli distribution is formulated to produce a new system which includes the information of the probability distribution. Based on the Lyapunov-Krasovskii functional method, the Jensen's integral inequality theory and linear matrix inequality (LMI) technique, several delay-distribution-dependent sufficient conditions are developed to guarantee that the chaotic neural networks with mixed random time-varying delays are exponentially synchronized in mean square. Furthermore, the derived results are given in terms of simplified LMI, which can be straightforwardly solved by Matlab. Finally, two numerical examples are proposed to demonstrate the feasibility and the effectiveness of the presented synchronization scheme.

Keywords:

Mathematics Subject Classification: Primary: 93B36, 93B52, 93C10, 93C55.

Citation:

Full Text(HTML)

Figure 1. (a) Chaotic behavior of the neural networks (3). (b) Chaotic behavior of the neural networks (4) without control input $ u(t) $

Download: Full-size image PowerPoint slide

Figure 2. (a) Chaotic behavior of the neural networks (4). (b)-(c) State trajectories of the neural networks (3) and (4). (d) Synchronization error trajectories of the state variables between the neural networks (3) and (4)

Download: Full-size image PowerPoint slide

Figure 3. (a) Chaotic behavior of the neural networks (3). (b) Chaotic behavior of the neural networks (4) without control input $ u(t) $

Download: Full-size image PowerPoint slide

Figure 4. (a) Chaotic behavior of the neural networks (4). (b)-(c) State trajectories of the neural networks (3) and (4). (d) Synchronization error trajectories of the state variables between the neural networks (3) and (4)

Download: Full-size image PowerPoint slide

Related Papers

Cited by

References

[1]	P. Balasubramaniam, V. Vembarasan and R. Rakkiyappan, Delay-dependent robust exponential state estimation of Markovian jumping fuzzy Hopfield neural networks with mixed random time-varying delays, Commun. Nonlinear Sci. Numer. Simul., 16 (2011), 2109-2129. doi: 10.1016/j.cnsns.2010.08.024.
[2]	H. Bao and J. Cao, Delay-distibution-dependent state estimation for discrete-time stochastic neural networks with random delay, Neural Network, 24 (2011), 19-28.
[3]	J. Cai, L. Shen and F. Wu, Adaptive control of a class of non-linear systems preceded by backlash-like hysteresis, Math. Struct. Comput. Sci., 24 (2014), e240504, 14 pp. doi: 10.1017/S0960129512000473.
[4]	A. Cichoki and R. Unbehauen, Neural Networks for Optimization and Signal Processing, John Wiley and Sons, 2003.
[5]	Q. Gan and Y. Liang, Synchronization of chaotic neural networks with time delay in the leakage term and parametric uncertainties based on sampled-data control, J. Franklin Inst., 349 (2012), 1955-1971. doi: 10.1016/j.jfranklin.2012.05.001.
[6]	M. Gilli, Strange attractors in delayed cellular neural networks, IEE Trans. Circuits Syst. I: Fundam. Theor. Appl., 40 (1993), 849-853. doi: 10.1109/81.251826.
[7]	K. Gu, V. L. Kharitonov and J. Chen, Stability of Time-Delay Systems, Birkhäuser Boston, Inc., Boston, MA, 2003. doi: 10.1007/978-1-4612-0039-0.
[8]	B. Hu, Q. Song, K. Li, Z. Zhao, Y. Liu and Fuad E. Alsaadi, Global $\mu$-synchronization of impulsive complex-valued neural networks with leakage delay and mixed time-varying delays, Neurocomputing, 307 (2018), 106-116.
[9]	T. Kwork and K. A. Smith, A unified framework for chaotic neural networks approaches to combinatorial optimization, IEE Trans. Neural Netw., 10 (1999), 978-981.
[10]	X. Li and S. Song, Research on synchronization of chaotic delayed neural networks with stochastic perturbation using impulsive control method, Commun. Nonlinear Sci. Numer. Simul., 19 (2014), 3892-3900. doi: 10.1016/j.cnsns.2013.12.012.
[11]	J.-N. Li, Y.-J. Su and C.-L. Wen, Stochastic reliable control of a class of networked control systems with actuator faults and input saturation, Int. J. Control, Autom., Syst., 12 (2014), 564-571. doi: 10.1007/s12555-013-0371-7.
[12]	Z. X. Liu, H. W. Yang and F. W. Chen, Mean square exponential synchronization of stochastic neutral type chaotic neural networks with mixed delay, International Journal of Mathematical and Computational Sciences, 8 (2011), 1298-1303.
[13]	H. Lu, Chaotic attractors in delayed neural networks, Phys. Lett. A, 298 (2002), 109-116. doi: 10.1016/S0375-9601(02)00538-8.
[14]	G. Nagamani and S. Ramasamy, Stochastic deissativity and passivity analysis for discrete-time neural networks with probabilistic time-varying delays in leakage term, Appl. Math. Comput., 289 (2016), 237-257. doi: 10.1016/j.amc.2016.05.004.
[15]	J. Nilsson, B. Bernhardsson and B. Wittenmark, Stochastic analysis and control of real-time systems with random time delays, Automatica J. IFAC, 34 (1998), 57-64. doi: 10.1016/S0005-1098(97)00170-2.
[16]	C. Peng and Y.-C. Tian, Improved delay-dependent robust stability criteria for uncertain systems with interval time-varing delay, IET Control Theory Appl., 2 (2008), 752-761. doi: 10.1049/iet-cta:20070362.
[17]	A. Pratap, R. Raja, Ji nde Cao, G. Rajchakit and Fuad E. Alsaadi, Further synchronization in finite time analysis for time-varying delayed fractional order memristive competitive neural networks with leakage delay, Neurocomputing, 317 (2018), 110-126. doi: 10.1016/j.neucom.2018.08.016.
[18]	Y. Tang, J. Fang and Q. Miao, On the exponential synchronization of stochastic jumping chaotic neural networks with mixed delays and sector-bounded nonlinearities, Neurocomputing, 721 (2009), 694-701.
[19]	G. Velmurugan, R. Rakkiyappan and J. Cao, Finite-time synchronization of fractional-order memristor-based neural networks with time delays, Neural Netw., 73 (2016), 36-46.
[20]	J. Wang, K. Shi, Q. Huang, S. Zhong and D. Zhang, Stochastic switched sampled-data control for synchronization of delayed chaotic neural networks with packet dropout, Appl. Math. Comput., 335 (2018), 211-230. doi: 10.1016/j.amc.2018.04.038.
[21]	Z. Wang, H. Shu, Y. Liu, D. W. C. Ho and X. Liu, Robust stability analysis of generalized neural networks with dicrete and distributed time delays, Chaos Solitons Fractals, 30 (2006), 886-896. doi: 10.1016/j.chaos.2005.08.166.
[22]	W. Wang, M. Yu, X. Luo, L. Liu, M. Yuan and W. Zhao, Synchronization of memristive BAM neural networks with leakage delay and additive time-varying delay components via sampled-data control, Chaos Solitons Fractals, 104 (2017), 84-97. doi: 10.1016/j.chaos.2017.08.011.
[23]	H. Wu, X. Zhang, R. Li and R. Yao, Finite-time synchronization of chaotic neural networks with mixed time-varying delays and stochastic disturbance, Memetic Comp., 7 (2015), 231-240. doi: 10.1007/s12293-014-0150-x.
[24]	S. Xu and T. Chen, Robust $H_{\infty}$ control for uncertain stochastic systems with state delay, IEEE Trans. Automat. Control, 47 (2002), 2089-2094. doi: 10.1109/TAC.2002.805670.
[25]	X. Zhang, X. Lv and X. Li, Sampled-data-based lag synchronization of chaotic delayed neural networks with impulsive control, Nonlinear Dyn., 90 (2017), 2199-2207. doi: 10.1007/s11071-017-3795-4.
[26]	C.-D. Zheng, Z. Wei and Z. Wang, Robustly adaptive synchronization for stochastic Markovian neural networks of neutral type with mixed mode-dependent delays, Neurocomputing, 171 (2016), 1254-1264. doi: 10.1016/j.neucom.2015.07.066.
[27]	F. Zou and J. A. Nossek, Bifurcation and chaos in cellular neural networks, IEE Trans. Circuits Syst. I: Fundam. Theor. Appl., 40 (1993), 166-173. doi: 10.1109/81.222797.