Research article

Novel fixed-time stabilization of quaternion-valued BAMNNs with disturbances and time-varying coefficients

  • Received: 16 January 2020 Accepted: 18 March 2020 Published: 23 March 2020
  • MSC : 92B20

  • In this paper, with the quaternion number and time-varying coefficients introduced into traditional BAMNNs, the model of quaternion-valued BAMNNs are formulated. For the first time, fixed-time stabilization of time-varying quaternion-valued BAMNNs is investigated. A novel fixedtime control method is adopted, in which the choice of the Lyapunov function is more general than in most previous results. To cope with the noncommutativity of the quaternion multiplication, two different fixed-time control methods are provided, a decomposition method and a non-decomposition method. Furthermore, to reduce the control strength and improve control efficiency, an adaptive fixed-time control strategy is proposed. Lastly, numerical examples are presented to demonstrate the effectiveness of the theoretical results.

    Citation: Ruoyu Wei, Jinde Cao, Jurgen Kurths. Novel fixed-time stabilization of quaternion-valued BAMNNs with disturbances and time-varying coefficients[J]. AIMS Mathematics, 2020, 5(4): 3089-3110. doi: 10.3934/math.2020199

    Related Papers:

  • In this paper, with the quaternion number and time-varying coefficients introduced into traditional BAMNNs, the model of quaternion-valued BAMNNs are formulated. For the first time, fixed-time stabilization of time-varying quaternion-valued BAMNNs is investigated. A novel fixedtime control method is adopted, in which the choice of the Lyapunov function is more general than in most previous results. To cope with the noncommutativity of the quaternion multiplication, two different fixed-time control methods are provided, a decomposition method and a non-decomposition method. Furthermore, to reduce the control strength and improve control efficiency, an adaptive fixed-time control strategy is proposed. Lastly, numerical examples are presented to demonstrate the effectiveness of the theoretical results.


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    [1] G. Simmons, Calculus Gems: Brief Lives and Memorable Mathematics, New York: USA: McGraw-Hill, 1992.
    [2] S. Adler, Quaternionic Quantum Mechanics and Quantum Fields, USA: Oxford Univ. Press, 1995.
    [3] C. Took and D. Mandic, The quaternion LMS algorithm for adaptive filtering of hypercomplex processes, IEEE T. Signal Process., 57 (2009), 1316-1327. doi: 10.1109/TSP.2008.2010600
    [4] C. Zou, K. Kou, Y. Wang, Quaternion collaborative and sparse representation with application to color face recognition, IEEE T. Image Process., 25 (2016), 3287-3302. doi: 10.1109/TIP.2016.2567077
    [5] Y. Xia, C. Jahanchahi, D. P. Mandic, Quaternion-valued echo state networks, IEEE T. Neur. Netw. Lear. Syst., 26 (2015), 663-673. doi: 10.1109/TNNLS.2014.2320715
    [6] T. Isokawa, T. Kusakabe, N. Matsui, et al. Quaternion neural network and its application. In: V. Palade, R. J. Howlett, L. Jain (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science, vol 2774. Springer, Berlin, Heidelberg.
    [7] S. Qin, J. Feng, J. Song, et al. A one-layer recurrent neural network for constrained complexvariable convex optimization, IEEE T. Neur. Netw. Lear. Syst., 29 (2018), 534-544. doi: 10.1109/TNNLS.2016.2635676
    [8] Z. Tu, J. Cao, A. Alsaedi, et al. Global dissipativity analysis for delayed quaternion-valued neural networks, Neural Netw., 89 (2017), 97-104. doi: 10.1016/j.neunet.2017.01.006
    [9] N. Li and J. Cao, Global dissipativity analysis of quaternion-valued memristor-based neural networks with proportional delay, Neurocomputing, 321 (2018), 103-113. doi: 10.1016/j.neucom.2018.09.030
    [10] Y. Liu, D. Zhang, J. Lu, Global exponential stability for quaternion-valued recurrent neural networks with time-varying delays, Nonlinear Dyn., 87 (2017), 553-565. doi: 10.1007/s11071-016-3060-2
    [11] Q. Song and X. Chen, Multistability analysis of quaternion-valued neural networks with time delays, IEEE T. Neur. Netw. Lear. Syst., 29 (2018), 5430-5440. doi: 10.1109/TNNLS.2018.2801297
    [12] X. Chen, Z. Li, Q. Song, et al. Robust stability analysis of quaternion-valued neural networks with time delays and parameter uncertainties, Neural Netw., 91 (2017), 55-65. doi: 10.1016/j.neunet.2017.04.006
    [13] X. Chen and Q. Song, State estimation for quaternion-valued neural networks with multiple time delays, IEEE T. Syst. Man Cybern., Syst., 49 (2019), 2278-2287. doi: 10.1109/TSMC.2017.2776940
    [14] Y. Liu, D. Zhang, J. Lou, et al. Stability analysis of quaternion-valued neural networks: decomposition and direct approaches, IEEE T. Neur. Netw. Lear. Syst., 29 (2018), 4201-4211. doi: 10.1109/TNNLS.2017.2755697
    [15] R. Wei and J. Cao, Fixed-time synchronization of quaternion-valued memristive neural networks with time delays, Neural Netw., 113 (2019), 1-10. doi: 10.1016/j.neunet.2019.01.014
    [16] B. Kosko, Adaptive bidirectional associative memories, Appl. Opt., 26 (1987), 4947-4960. doi: 10.1364/AO.26.004947
    [17] B. Kosko, Bidirectional associative memories, IEEE T. Syst. Man Cybern., Syst., 18 (1988), 49-60. doi: 10.1109/21.87054
    [18] X. Li, D. O'Regan, H. Akca, Global exponential stabilization of impulsive neural networks with unbounded continuously distributed delays, IMA J. Appl. Math., 80 (2015), 85-99. doi: 10.1093/imamat/hxt027
    [19] Y. Li and C. Li, Matrix measure strategies for stabilization and synchronization of delayed BAM neural network, Nonlinear Dyn., 84 (2016), 1759-1770. doi: 10.1007/s11071-016-2603-x
    [20] C. Chen, L. Li, H. Peng, et al. Fixed-time synchronization of memristor-based BAM neural networks with time-varying discrete delay, Neural Netw., 96 (2017), 47-54. doi: 10.1016/j.neunet.2017.08.012
    [21] D. Wang, L. Huang, L. Tang, Dissipativity and synchronization of generalized BAM neural networks with multivariate discontinuous activations, IEEE T. Neur. Netw. Lear. Syst., 29 (2018), 3815-3827. doi: 10.1109/TNNLS.2017.2741349
    [22] Z. Zhang, R. Guo, X. Liu, et al. Lagrange exponential stability of complex-valued BAM neural networks with time-varying delays, IEEE T. Syst. Man Cybern. Syst., (2018), 1-14.
    [23] Y. Cao, R. Samidurai, R. Sriraman, Robust passivity analysis for uncertain neural networks with leakage delay and additive time-varying delays by using general activation function, Math. Comput. Simulat., 155 (2019), 57-77. doi: 10.1016/j.matcom.2017.10.016
    [24] Y. Cao, R. Sriraman, N. Shyamsundarraj, et al. Robust stability of uncertain stochastic complexvalued neural networks with additive time-varying delays, Math. Comput. Simulat., 171 (2020), 207-220. doi: 10.1016/j.matcom.2019.05.011
    [25] X. Yang and X. Li, Finite-time stability of linear non-autonomous systems with time-varying delays, Advances in Difference Equations, 2018 (2018), 101.
    [26] L. Wang, Y. Shen, G. Zhang, Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks, IEEE T. Neur. Netw. Lear. Syst., 28 (2017), 2648-2659.
    [27] X. Liu, D. Ho, Q. Song, et al. Finite-/fixed-time robust stabilization of switched discontinuous systems with disturbances, Nonlinear Dyn., 90 (2017), 2057-2068. doi: 10.1007/s11071-017-3782-9
    [28] R. Wei, J. Cao, A. Alsaedi, Finite-time and fixed-time synchronization analysis of inertial memristive neural networks with time-varying delays, Cogn. Neurodyn., 12 (2018), 121-134. doi: 10.1007/s11571-017-9455-z
    [29] J. Hu, G. Sui, X. Lv, et al. Fixed-time control of delayed neural networks with impulsive perturbations, Nonlinear Analysis: Modelling and Control, 23 (2018), 904-920. doi: 10.15388/NA.2018.6.6
    [30] Z. Wang, J. Cao, Z. Cai, et al. Anti-synchronization in fixed time for discontinuous reactiondiffusion neural networks with time-varying coefficients and time delay, IEEE T. Cybernetics, (2019), 1-12.
    [31] R. Wei, J. Cao, M. Abdel-Aty, Fixed-time synchronization of second-order MNNs in quaternion field, IEEE T. Syst. Man Cybern. Syst., (2019), 1-12.
    [32] L. Wang, Y. Shen, Q. Yin, et al. Adaptive synchronization of memristor-based neural networks with time-varying delays, IEEE T. Neur. Netw. Lear. Syst., 26 (2015), 2033-2042. doi: 10.1109/TNNLS.2014.2361776
    [33] C. Chen, L. Li, H. Peng, et al. Adaptive synchronization of memristor-based BAM neural networks with mixed delays, Appl. Math. Comput., 322 (2018), 100-110.
    [34] Z. Yang, B. Luo, D. Liu, et al. Adaptive synchronization of delayed memristive neural networks with unknown parameters, IEEE T. Syst. Man Cybern. Syst., 50 (2020), 539-549. doi: 10.1109/TSMC.2017.2778092
    [35] H. Zhang, N. Pal, Y. Sheng, et al. Distributed adaptive tracking synchronization for coupled reaction-diffusion neural network, IEEE T. Neur. Netw. Lear. Syst., 30 (2019), 1462-1475. doi: 10.1109/TNNLS.2018.2869631
    [36] A. Polyakov, Adaptive fuzzy neural network control for a constrained robot using impedance learning, IEEE T. Neur. Netw. Lear. Syst., 29 (2018), 1174-1186. doi: 10.1109/TNNLS.2017.2665581
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