Research article

Synchronization criteria for neutral-type quaternion-valued neural networks with mixed delays

  • Received: 27 March 2021 Accepted: 19 May 2021 Published: 24 May 2021
  • MSC : 34D06, 62M45, 93C23

  • In this paper, the problem of synchronization for neutral-type quaternion-valued neural networks (NQVNNs) with mixed delays is investigated. By making full use of the information of the time-delay state, a linear feedback controller and a novel nonlinear feedback controller are constructed to research the global synchronization and finite-time synchronization of the system respectively. In the case where the activation function of the network is not required to be separated into two complex parts or four real parts, the sufficient conditions of synchronization of NQVNNs are acquired based on establishing appropriate Lyapunov-Krasovskii functional, applying the synchronization method of drive-response and some inequality techniques. The obtained delay-dependent synchronization results are less conservative than some existing ones via numerical example comparisons. Two numerical examples with simulations are provided to verify the effectiveness of the obtained results.

    Citation: Shuang Li, Xiao-mei Wang, Hong-ying Qin, Shou-ming Zhong. Synchronization criteria for neutral-type quaternion-valued neural networks with mixed delays[J]. AIMS Mathematics, 2021, 6(8): 8044-8063. doi: 10.3934/math.2021467

    Related Papers:

  • In this paper, the problem of synchronization for neutral-type quaternion-valued neural networks (NQVNNs) with mixed delays is investigated. By making full use of the information of the time-delay state, a linear feedback controller and a novel nonlinear feedback controller are constructed to research the global synchronization and finite-time synchronization of the system respectively. In the case where the activation function of the network is not required to be separated into two complex parts or four real parts, the sufficient conditions of synchronization of NQVNNs are acquired based on establishing appropriate Lyapunov-Krasovskii functional, applying the synchronization method of drive-response and some inequality techniques. The obtained delay-dependent synchronization results are less conservative than some existing ones via numerical example comparisons. Two numerical examples with simulations are provided to verify the effectiveness of the obtained results.



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    [1] X. M. Wang, Introduction to neural networks, China Sci. J., 2017.
    [2] X. F. Chen, L. J. Li, Z. S. Li, Robust stability analysis of quaternion-valued neural networks via LMI approach, Adv. Diff. Equ., 2018 (2018), 131.
    [3] R. Rakkiyappan, K. Udhayakumar, G. Velmurugan, J. D. Cao, A. Ahmed, Stability and Hopf bifurcation analysis of fractional-order complex-valued neural networks with time delays, Adv. Diff. Equ., 2017 (2017), 225.
    [4] X. X. Zhang, C. D. Li, T. W. Huang, Impacts of state-dependent impulses on the stability of switching Cohen-Grossberg neural networks, Adv. Diff. Equ., 2017 (2017), 316.
    [5] X. M. Yu, X. M. Wang, S. M. Zhong, K. B. Shi, Further results on delay-dependent stability for continuous system with two additive time-varying delay components, Isa T. 65 (2016), 9–18.
    [6] G. Rajchakit, P. Chanthorn, M. Niezabitowski, R. Raja, D. Baleanu, A. Pratap, Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks, Neurocomputing, 417 (2020), 290–301.
    [7] U. Humphries, G. Rajchakit, P. Kaewmesri, P. Chanthorn, R. Sriraman, R. Samidurai, Global stability analysis of fractional-order quaternion-valued bidirectional associative memory neural networks, Mathematics, 8 (2020), 801.
    [8] G. Rajchakit, P. Chanthorn, P. Kaewmesri, R. Sriraman, C. P. Lim, Global mittag-leffler stability and stabilization analysis of fractional-order quaternion-valued memristive neural networks, Mathematics, 8 (2020), 422.
    [9] U. Humphries, G. Rajchakit, P. Kaewmesri, P. Chanthorn, R. Sriraman, R. Samidurai, Stochastic memristive quaternion-valued neural networks with time delays: An analysis on mean square exponential input-to-state stability, Mathematics, 8 (2020), 815.
    [10] T. Isokawa, T. Kusakabe, N. Matsui, F. Peper, Quaternion neural network and its application, In: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, Berlin, Heidelberg: Springe, 2003.
    [11] T. Minemoto, T. Isokawa, H. Nishimura, N. Matsui, Quaternionic multistate Hopfield neural network with extended projection rule, Artif. Life Robotics, 21 (2016), 106–111.
    [12] X. F. Chen, Q. K. Song, Z. S. Li, Design and analysis of quaternion-valued neural networks for associative memories, IEEE T. Syst. Cybern. Syst., 48 (2018), 2305–2314.
    [13] Y. Liu, D. D. Zhang, J. G. Lou, J. Q. Lu, J. D. Cao, Stability analysis of quaternion-valued neural networks: Decomposition and direct approaches, IEEE T. Neur. Net. Lear., 29 (2017), 4201–4211.
    [14] G. Rajchakit, R. Sriraman, Robust passivity and stability analysis of uncertain complex-valued impulsive neural networks with time-varying delays, Neural Process Lett., 53 (2021), 581–606.
    [15] R. Sriraman, G. Rajchakit, C. P. Lim, P. Chanthorn, R. Samidurai, Discrete-time stochastic quaternion-valued neural networks with time delays: An asymptotic stability analysis, Symmetry, 12 (2020), 936.
    [16] S. Gupta, Linear quaternion equations with application to spacecraft attitude propagation, IEEE Aerosp. Conf. Proc., 1 (1998), 69–76.
    [17] L. C. Luo, H. Feng, L. J. Ding, Color image compression based on quaternion neural network principal component analysis, 2010 International Conference on Multimedia Technology, 2010, 1-4.
    [18] H. Kusamichi, T. Isokawa, N. Matsui, Y. Ogawa, K. Maeda, A new scheme for colour night vision by quaternion neural network, 2nd International Conference on Autonomous Robots and Agents, 2004,101–106.
    [19] C. Maharajan, R. Raja, J. D. Cao, G. Rajchakit, Z. W. Tu, A. Alsaedi, LMI-based results on exponential stability of BAM-type neural networks with leakage and both time-varying delays: A non-fragile state estimation approach, Appl. Math. Comput., 326 (2018), 33–55.
    [20] J. Liu, J. G. Jian, B. X. Wang, Stability analysis for BAM quaternion-valued inertial neural networks with time delay via nonlinear measure approach, Math. Comput. Simul., 174 (2020), 134–152.
    [21] X. F. Chen, Z. S. Li, Q. K. Song, J. Hu, Y. S. Tan, Robust stability analysis of quaternion-valued neural networks with time delays and parameter uncertainties, Neural Networks, 91 (2017), 55–65.
    [22] X. W. Liu, T. P. Chen, Global exponential stability for complex-valued recurrent neural networks with asynchronous time delays, IEEE T. Neur. Net. Lear., 27 (2016), 593–606.
    [23] W. L. Lu, T.P. Chen, Synchronization of coupled connected neural networks with delays, IEEE Transactions on Circuits & Systems I Regular Papers, 51 (2004), 2491–2503.
    [24] J. Liu, J. G. Jian, Global dissipativity of a class of quaternion-valued BAM neural networks with time delay, Neurocomputing, 349 (2019), 123–132.
    [25] L. Li, W. S. Chen, Exponential stability analysis of quaternion-valued neural networks with proportional delays and linear threshold neurons: Continuous-time and discrete-time cases, Neurocomputing, 381 (2020), 152–166.
    [26] Q. K. Song, Y. X. Chen, Z. J. Zhao, Y. R. Liu, F. E. Alsaadi, Robust stability of fractional-order quaternion-valued neural networks with neutral delays and parameter uncertainties, Neurocomputing, 420 (2021), 70–81.
    [27] J. L. Shu, L. L. Xiong, T. Wu, Z. X. Liu, Stability analysis of quaternion-valued neutral-type neural networks with time-varying delay, Mathematics, 7 (2019), 101.
    [28] D. Y. Liu, Stability analysis of switched neutral systems, University of Electronic Science and Technology of China, 2010.
    [29] Z. Tu, X. Yang, L. Wang, B. Ding, Stability and stabilization of quaternion-valued neural networks with uncertain time-delayed impulses: Direct quaternion method, Physica A., 535 (2019), 122358.
    [30] Z. W. Tu, D. D. Wang, X. S. Yang, J. D. Cao, Lagrange stability of memristive quaternion-valued neural networks with neutral items, Neurocomputing, 399 (2020), 380–389.
    [31] Q. Y. Zhu, Adaptive synchronization control of mode-dependent stochastic neutral-type neural network, Donghua University, 2014.
    [32] H. Zhang, X. Y. Wang, X. H. Lin, Synchronization of complex-valued neural network with sliding mode control, J. Franklin I., 353 (2016), 345–358.
    [33] B. X. Hu, Q. K. Song, K. L. Li, Z. J. Zhao, Y. R. Liu, F. E. Alsaadie, Global $ \mu $-synchronization of impulsive complex-valued neural networks with leakage delay and mixed time-varying delays, Neurocomputing, 307 (2018), 106–116.
    [34] L. B. Liu, X. X. You, X. P. GAO, Global synchronization control of quaternion neural networks with mixed delays, Control Theory Appl., 36 (2019), 1360–1368.
    [35] D. Y. Lin, X. F. Chen, G. P. Yu, Z. S. Li, X. N. Xia, Global exponential synchronization via nonlinear feedback control for delayed inertial memristor-based quaternion-valued neural networks with impulses, Appl. Math. Comput., 401 (2021), 126093.
    [36] H. Deng, H. B. Bao, Fixed-time synchronization of quaternion-valued neural networks, Physica A., 527 (2019), 121351.
    [37] H. Pu, L. Q. Wang, Control synchronization of random perturbation neural network with reaction diffusion term in finite time, Anhui Normal University, 42 (2019), 442–450.
    [38] X. F. Chen, Q. K. Song, Global stability of complex-valued neural networks with both leakage time delay and discrete time delay on time scales, Neurocomputing., 121 (2013), 254–264.
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