Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2020 / Article
Special Issue

Control of Networked Systems with Engineering Applications 2020

View this Special Issue

Research Article | Open Access

Volume 2020 |Article ID 5986018 | https://doi.org/10.1155/2020/5986018

Jie Fang, Yin Zhang, Danying Xu, Junwei Sun, "Synchronization of Time Delay Coupled Neural Networks Based on Impulsive Control", Mathematical Problems in Engineering, vol. 2020, Article ID 5986018, 8 pages, 2020. https://doi.org/10.1155/2020/5986018

Synchronization of Time Delay Coupled Neural Networks Based on Impulsive Control

Guest Editor: Hou-Sheng Su
Received25 Jun 2020
Accepted30 Jul 2020
Published18 Aug 2020

Abstract

This paper investigates the impulsive synchronization of time delay coupled neural networks. Based on the Lyapunov stability theory and impulsive control method, a distributed delayed impulsive controller is designed to realize synchronization of the coupled neural networks. A new impulsive delayed inequality is proposed, where the control effect of distributed delayed impulses is fully considered. In addition, a suitable Lyapunov-like function is established to prove the stability of the synchronization system. Numerical simulation examples are introduced to illustrate the effectiveness and feasibility of the main results.

1. Introduction

Neural networks are a type of mathematical model that simulates the thinking patterns of the human brain, which is used to imitate the structure and thinking mode of neural network in the human brain [16]. The neural network learning is to abstract and simplify the human brain from the microscopic structure and function. It is one of the ways to realize artificial intelligence. Since 1980s, the research of neural network has made great progress [710]. Neural network synchronization research is one of the most important research directions of the neural network. Until now, various synchronization control schemes have been proposed, such as impulsive control [11, 12], adaptive control [13, 14], sliding mode control [15, 16], switching control [1719], and pinning control [20].

Compared with continuous control, the structure of impulsive control is simpler. The small intermittent control input can achieve the expected control performance [2124]. The key idea of impulse control is to convert the state of a continuous dynamic system into discontinuous forms through a discrete control input. Since impulsive control allows the system to admit discrete inputs and effectively save network bandwidth resources, it has been studied by many scholars. Liu et al. [25] realized the uniform synchronization of chaotic dynamics system by designing three levels of event-triggered impulsive control. He et al. [26] realized the secure synchronization of multiagent systems under attacks through impulsive control. Qian et al. [27] realized the synchronization of multiagent systems through impulsive control. What is more, there always exists time delay in most practical systems, which should not be ignored in the study of neural network synchronization. For the time delay coupled neural network, the synchronization problem was first discussed in reference [28]. Xu et al. [29] realized the time delay synchronization of the chaotic neural networks based on impulsive control. Xie et al. [30] investigated the synchronization of time-varying delays coupled reaction-diffusion neural networks with pinning impulsive control. Wei et al. [31] realized synchronization of the coupled reaction-diffusion neural networks with time-varying delay by impulsive control. Li et al. [32] investigated the master-slave exponential synchronization of the neural networks with time-varying delays via discontinuous impulsive control. Until now, there are many control methods to realize the synchronization of neural networks. Among them, the research on time-delay impulsive control mainly concentrated on a single neural network system, while the research on the synchronization of coupled neural network with time-delay impulsive control is relatively rare.

In this paper, the problem of synchronization of delay coupled neural networks under distributed delay impulsive control is studied. Firstly, a time delay coupled neural networks model with N identical nodes is constructed. Secondly, based on the impulsive control method, a distributed delay impulsive controller is designed to achieve synchronization between the drive and response system. Thirdly, through the designed impulsive controller and impulsive delayed inequality, the stability of the synchronization system is analyzed. Finally, numerical examples are given to illustrate the effectiveness of the developed method.

The rest of this paper is organized as follows. In Section 2, synchronization problem of the time delay coupled neural networks and some premises are proposed. Section 3 presents the main results with proof. A simulation example is provided in Section 4 to illustrate the main results. The summary of this paper is given in Section 5.

2. Problem Statement

Considering a neural networks array consisting of N identical nodes, in which the dynamics of the i-th () node is described by the following neural networks:where denotes the neuron state vector of the i-th dynamical node, ; denotes the positive definite diagonal matrix; , represent the connection weight matrix and the delayed connection weight matrix, respectively; represents the neuron activation function satisfying for all , , , where are Lipschitz constants; is the constant coupling matrix; is the inner coupling matrix; represents coupling strength; is the initial condition; denotes the time delay occurring in transmission process; is an external input.

Let (1) be the drive system and the response system is constructed as follows:where denotes the neuron state vector of the i-th dynamical node, ; is the impulsive control input; is the initial condition.

Then, we will design a delayed impulse input to synchronize drive system (1) and response system (2). The impulsive controller is designed bywhere ; is a gain matrix to be designed; denote the distributed time delays in impulse input; is the Delta function.

When and the controller , the error derivative is

When , the error derivative iswhere . The above dynamical networks can be rewritten as the following Kronecker product form:where denotes the Kronecker product.

Definition 1. (see [33]). The exponential convergence criterion of the distributed delayed impulsive inequality is given:where , are constants. Distributed delays satisfy , where is a real constant.

Lemma 1 (see [34]). For the same dimension matrices H, M and the constant , then

Assumption 1. There exist positive constants , matrix , and diagonal matrices , the following conditions hold:where , .

3. Synchronization Analysis

In this part, we use the Lyapunov stability method and impulsive delayed inequality (7) to derive our main results.

Theorem 1. System (2) can achieve the impulsive synchronization with system (1) under delayed impulsive controller (3), if Assumption 1 holds.

Proof 1. Consider the Lyapunov-like function:

When , the derivative of along the trajectory of system (6) can be calculated as follows:

Based on Lemma 1, the inequality , , we can perform the following calculation:

Changing formula (13) to Kronecker product, we can get

Similarly, it gives that

From formulas (14) and (15), formula (9) can be changed towhere ; the following conditions are established:

When , the derivative of along the trajectory of system (6) can be calculated as follows:

From (14) and (15), we have

Based on Lemma 1, we can perform the following calculation:

It follows from (10) that there exists an inequality such that , which together with (20) implies thatwhere . Then, it can be deduced that . According to formulas (9) and (21), we can derive that

According to the Lyapunov stability theory, we can obtain as , which means that system (2) can achieve the impulsive synchronization with system (1) under impulsive controller (3). This completes the proof.

4. Numerical Simulation

In the simulation, we studied the synchronization problem of the coupled neural network with three nodes, namely, N = 3. The activation functions are chosen as .

Consider the following neural networks:

Consider the following response system:where , , , , , initial condition , , , , , , and , and the parameter matrices C, A, B, D, and are given by

When there is no control input (i.e., ), system (24) cannot be synchronized with system (23), see Figure 1. Next, we consider distributed delayed impulsive control to achieve the synchronization between system (23) and system (24). The control input is given by (3) with . Choose , using the MATLAB LMI toolbox, and the following feasible solutions can be derived:

Thus, the gain matrix K is derived as follows:

For simulation, if we take , , then the synchronization error is shown in Figure 2.

5. Conclusion

In this paper, the synchronization of coupled neural networks is studied by distributed delayed impulsive control. Based on the impulsive control theory and Lyapunov stability theory, a distributed delayed impulsive controller is proposed. The proposed inequality fully considers the synchronization of the response system and the drive system under different time conditions. The numerical simulation proves the feasibility and effectiveness of our proposed scheme. In the future, we will deal with finite time synchronization of coupled neural networks through distributed delayed impulsive control.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The work was supported by the National Natural Science Foundation of China (Grant no. 61775198), Science and Technology Project of Henan Province (Grant nos. 192102210083 and 202102210317), Key Scientific Research Projects of Universities in Henan Province (Grant no. 20A413012), and Science and Technology Innovation Team Project of Henan Province (Grant no. 19IRTSTHN013).

References

  1. S. Arik, “An analysis of exponential stability of delayed neural networks with time varying delays,” Neural Networks, vol. 17, no. 7, pp. 1027–1031, 2004. View at: Publisher Site | Google Scholar
  2. X. Ye, J. Mou, C. Luo, and Z. Wang, “Dynamics analysis of Wien-bridge hyperchaotic memristive circuit system,” Nonlinear Dynamics, vol. 92, no. 3, pp. 923–933, 2018. View at: Publisher Site | Google Scholar
  3. L. O. Chua and L. Yang, “Cellular neural networks: applications,” IEEE Transactions on Circuits and Systems, vol. 35, no. 10, pp. 1273–1290, 1998. View at: Google Scholar
  4. J. W. Sun, G. Y. Han, Z. G. Zeng, and Y. F. Wang, “Memristor-based neural network circuit of full-function pavlov associative memory with time delay and variable learning rate,” IEEE Transactions on Cybernetics, vol. 10, p. 1109, 2019. View at: Google Scholar
  5. X. Liu, X. Shen, Y. Zhang, and Q. Wang, “Stability criteria for impulsive systems with time delay and unstable system matrices,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 54, no. 10, pp. 2288–2298, 2007. View at: Publisher Site | Google Scholar
  6. H. Su, Y. Sun, and Z. Zeng, “Semiglobal observer-based non-negative edge consensus of networked systems with actuator saturation,” IEEE Transactions on Cybernetics, vol. 50, no. 6, pp. 2827–2836, 2020. View at: Publisher Site | Google Scholar
  7. U. Fory and M. Bodnar, “Time delays in proliferation process for solid avascular tumour,” Math and Computer Modelling, vol. 37, no. 11, pp. 1201–1209, 2003. View at: Google Scholar
  8. W. Yu, J. Cao, and G. Chen, “Stability and Hopf bifurcation of a general delayed recurrent neural network,” IEEE Transactions on Neural Networks, vol. 19, no. 5, pp. 845–854, 2008. View at: Publisher Site | Google Scholar
  9. H. Su, J. Zhang, and Z. Zeng, “Formation-containment control of multi-robot systems under a stochastic sampling mechanism,” Science China Technological Sciences, vol. 63, no. 6, pp. 1025–1034, 2020. View at: Publisher Site | Google Scholar
  10. F. F. Yang, J. Mou, J. Liu, C. G. Ma, and H. Z. Yan, “Characteristic analysis of the fractional-order hyperchaotic complex system and its image encryption application,” Signal Processing, vol. 169, 2020. View at: Google Scholar
  11. Q. Zhang, J. Lu, and J. Zhao, “Impulsive synchronization of general continuous and discrete-time complex dynamical networks,” Communications in Nonlinear Science and Numerical Simulation, vol. 15, no. 10, pp. 1063–1070, 2010. View at: Publisher Site | Google Scholar
  12. J. Zhou, L. Xiang, and Z. Liu, “Synchronization in complex delayed dynamical networks via impulsive control,” Physica A: Statistical Mechanics and Its Applications, vol. 384, no. 2, pp. 684–692, 2007. View at: Publisher Site | Google Scholar
  13. W. Xu, S. Zhu, X. Fang, and W. Wang, “Adaptive synchronization of memristor-based complex-valued neural networks with time delays,” Neurocomputing, vol. 364, no. 28, pp. 119–128, 2019. View at: Publisher Site | Google Scholar
  14. Q. Wang and J. L. Wu, “Finite-time output synchronization of undirected and directed coupled neural networks with output coupling,” IEEE Transactions on Neural Neworks and Learning Systems, vol. 10, p. 1109, 2020. View at: Google Scholar
  15. M. P. Aghababa, K. Sohrab, and G. Alizadeh, “Finite-time synchronization of two different chaotic systems with unknown parameters via sliding mode technique,” Applied Mathematical Modelling, vol. 35, no. 10, pp. 3080–3091, 2011. View at: Publisher Site | Google Scholar
  16. J. Sun, Y. Wu, G. Cui, and Y. Wang, “Finite-time real combination synchronization of three complex-variable chaotic systems with unknown parameters via sliding mode control,” Nonlinear Dynamics, vol. 88, no. 3, pp. 1677–1690, 2017. View at: Publisher Site | Google Scholar
  17. P. Selvaraj, R. Sakthivel, and O. M. Kwon, “Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation,” Neural Networks, vol. 105, pp. 154–165, 2018. View at: Publisher Site | Google Scholar
  18. D. Zhang, J. Cheng, J. Cao, and D. Zhang, “Finite-time synchronization control for semi-Markov jump neural networks with mode-dependent stochastic parametric uncertainties,” Applied Mathematics and Computation, vol. 344-345, no. 1, pp. 230–242, 2019. View at: Publisher Site | Google Scholar
  19. Y. Wu, J. Cao, Q. Li, A. Alsaedi, and F. E. Alsaadi, “Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching,” Neural Networks, vol. 85, pp. 128–139, 2017. View at: Publisher Site | Google Scholar
  20. Q. Song, J. Cao, and F. Liu, “Pinning synchronization of linearly coupled delayed neural networks,” Mathematics and Computers in Simulation, vol. 86, pp. 39–51, 2012. View at: Publisher Site | Google Scholar
  21. W.-H. Chen and W. X. Zheng, “Exponential stability of nonlinear time-delay systems with delayed impulse effects,” Automatica, vol. 47, no. 5, pp. 1075–1083, 2011. View at: Publisher Site | Google Scholar
  22. Z.-H. Guan, G.-S. Han, J. Li, D.-X. He, and G. Feng, “Impulsive multiconsensus of second-order multiagent networks using sampled position data,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 11, pp. 2678–2688, 2015. View at: Publisher Site | Google Scholar
  23. Z. Huang, J. Cao, J. Li, and H. Bin, “Quasi-synchronization of neural networks with parameter mismatches and delayed impulsive controller on time scales,” Nonlinear Analysis: Hybrid Systems, vol. 33, pp. 104–115, 2019. View at: Publisher Site | Google Scholar
  24. X. Yang, X. Li, X. Li, Q. Xi, and P. Duan, “Review of stability and stabilization for impulsive delayed systems,” Mathematical Biosciences & Engineering, vol. 15, no. 6, pp. 1495–1515, 2018. View at: Publisher Site | Google Scholar
  25. B. Liu, Z. Sun, Y. Luo, and Y. Zhong, “Uniform synchronization for chaotic dynamical systems via event-triggered impulsive control,” Physica A: Statistical Mechanics and Its Applications, vol. 531, no. 1, p. 121725, 2019. View at: Publisher Site | Google Scholar
  26. W. He, X. Gao, W. Zhong, and F. Qian, “Secure impulsive synchronization control of multi-agent systems under deception attacks,” Information Sciences, vol. 459, pp. 354–368, 2018. View at: Publisher Site | Google Scholar
  27. T. Qian, T. Yu, and B. Cui, “Adaptive synchronization of multi-agent systems via variable impulsive control,” Journal of the Franklin Institute, vol. 355, no. 15, pp. 7490–7508, 2018. View at: Publisher Site | Google Scholar
  28. W. Lu and T. Chen, “Synchronization of coupled connected neural networks with delays,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 51, no. 12, pp. 2491–2503, 2004. View at: Publisher Site | Google Scholar
  29. Z. Xu, D. Peng, and X. Li, “Synchronization of chaotic neural networks with time delay via distributed delayed impulsive control,” Neural Networks, vol. 118, pp. 332–337, 2019. View at: Publisher Site | Google Scholar
  30. X. Xie, X. Liu, H. Xu, X. Luo, and G. Liu, “Synchronization of coupled reaction-diffusion neural networks: delay-dependent pinning impulsive control,” Communications in Nonlinear Science and Numerical Simulation, vol. 79, Article ID 104905, 2019. View at: Publisher Site | Google Scholar
  31. P.-C. Wei, J.-L. Wang, Y.-L. Huang, B.-B. Xu, and S.-Y. Ren, “Impulsive control for the synchronization of coupled neural networks with reaction-diffusion terms,” Neurocomputing, vol. 207, no. 26, pp. 539–547, 2016. View at: Publisher Site | Google Scholar
  32. X. Li, J.-a. Fang, and H. Li, “Master-slave exponential synchronization of delayed complex-valued memristor-based neural networks via impulsive control,” Neural Networks, vol. 93, pp. 165–175, 2017. View at: Publisher Site | Google Scholar
  33. Y. Kan, J. Lu, J. Qiu, and J. Kurths, “Exponential synchronization of time-varying delayed complex-valued neural networks under hybrid impulsive controllers,” Neural Networks, vol. 114, pp. 157–163, 2019. View at: Publisher Site | Google Scholar
  34. S. Boyd, L. E. Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequality in Systems and Control Theory, SIAM, Philadelphia, PA, USA, 1994.

Copyright © 2020 Jie Fang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views101
Downloads229
Citations

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.