Theroy article Special Issues

Projective synchronization for quaternion-valued memristor-based neural networks under time-varying delays

  • Received: 20 August 2024 Revised: 08 October 2024 Accepted: 12 October 2024 Published: 17 October 2024
  • In this paper, the projective synchronization of quaternion-valued memristor-based neural networks with time-varing delays was studied. First, by utilizing set-valued map and differential inclusion theories, we reformulated the networks as an uncertain system with interval parameters. Then, through designing a novel controller and utilizing Lyapunov function and Young's inequality, several new synchronization conditions for projection synchronization of quaternion-valued memristor-based neural networks were obtained. Finally, the effectiveness of this method was demonstrated through a numerical example, underscoring its practical applicability.

    Citation: Jun Guo, Yanchao Shi, Yanzhao Cheng, Weihua Luo. Projective synchronization for quaternion-valued memristor-based neural networks under time-varying delays[J]. Networks and Heterogeneous Media, 2024, 19(3): 1156-1181. doi: 10.3934/nhm.2024051

    Related Papers:

  • In this paper, the projective synchronization of quaternion-valued memristor-based neural networks with time-varing delays was studied. First, by utilizing set-valued map and differential inclusion theories, we reformulated the networks as an uncertain system with interval parameters. Then, through designing a novel controller and utilizing Lyapunov function and Young's inequality, several new synchronization conditions for projection synchronization of quaternion-valued memristor-based neural networks were obtained. Finally, the effectiveness of this method was demonstrated through a numerical example, underscoring its practical applicability.



    加载中


    [1] L. O. Chua, Memristor-the missing circuit element, IEEE Trans. Circuit Theory, 18 (1971), 507–519. https://doi.org/10.1109/TCT.1971.1083337 doi: 10.1109/TCT.1971.1083337
    [2] H. Bao, Y. Zhang, W. Liu, B. Bao, Memristor synapse-coupled memristive neuron network: Synchronization transition and occurrence of chimera, Nonlinear Dyn., 100 (2020), 937–950. https://doi.org/10.1007/s11071-020-05529-2 doi: 10.1007/s11071-020-05529-2
    [3] F. Wei, G. Chen, W. Wang, Finite-time synchronization of memristor neural networks via interval matrix method, Neural Networks, 127 (2020), 7–18. https://doi.org/10.1016/j.neunet.2020.04.003 doi: 10.1016/j.neunet.2020.04.003
    [4] D. Ding, Z. You, Y. Hu, Z. Yang, L. Ding, Finite-time synchronization for fractional-order memristor-based neural networks with discontinuous activations and multiple delays, Mod. Phys. Lett. B, 34 (2020), 2050162. https://doi.org/10.1142/S0217984920501626 doi: 10.1142/S0217984920501626
    [5] U. Bhatti, Z. Yu, L. Yuan, Z. Zeeshan, M. Bhatti, M. Anum, et al., Geometric algebra applications in geospatial artificial intelligence and remote sensing image processing, IEEE Access, 8 (2020), 155783–155796. https://doi.org/10.1109/ACCESS.2020.3018544 doi: 10.1109/ACCESS.2020.3018544
    [6] L. Hua, Y. Qiang, J. Gu, L. Chen, X. Zhang, H. Zhu, Mechanical fault diagnosis using color image recognition of vibration spectrogram based on quaternion invariable moment, Math. Probl. Eng., 2015 (2015), 1–11. https://doi.org/10.1155/2015/702760 doi: 10.1155/2015/702760
    [7] M. Hasan, B. P. Mandal, New scattering features of quaternionic point interaction in non-Hermitian quantum mechanics, J. Math. Phys., 61 (2020), 032104. https://doi.org/10.1063/1.5117873 doi: 10.1063/1.5117873
    [8] R. J. Goodman, Digital simulation of aerospace vehicle flight path dynamics using quaternions, in Prague International Astronautical Federation Congress, 1977.
    [9] H. Wang, G. Wei, S. Wen, T. Huang, Impulsive disturbance on stability analysis of delayed quaternion-valued neural networks, Neurocomputing, 390 (2021), 125680. https://doi.org/10.1016/j.amc.2020.125680 doi: 10.1016/j.amc.2020.125680
    [10] J. Shu, B. Wu, L. Xiong, T. Wu, H. Zhang, Stochastic stabilization of Markov jump quaternion-valued neural network using sampled-data control, Appl. Math. Comput., 400 (2021), 1260414. https://doi.org/10.1016/j.amc.2021.126041 doi: 10.1016/j.amc.2021.126041
    [11] Y. Zhang, L. Zhou, Novel global polynomial stability criteria of impulsive complex-valued neural networks with multi-proportional delays, Neural Comput. Appl., 34 (2022), 2913–2924. https://doi.org/10.1007/s00521-021-06555-w doi: 10.1007/s00521-021-06555-w
    [12] S. Wang, Y. Shi, J. Guo, Exponential stability of a class of quaternion-valued memristor-based neural network with time-varying delay via M-matrix, Math. Methods Appl. Sci., 2024. https://doi.org/10.1002/mma.10486
    [13] Q. Song, Y. Chen, Z. Zhao, Y. Liu, F. Alsaadi, Robust stability of fractional-order quaternion-valued neural networks with neutral delays and parameter uncertainties, Neurocomputing, 420 (2021), 70–81. https://doi.org/10.1016/j.neucom.2020.08.059 doi: 10.1016/j.neucom.2020.08.059
    [14] R. Li, J. Cao, N. Li, Stop and go strategy for Lagrange stability of quaternion-valued memristive neural networks, Math. Methods Appl. Sci., 46 (2023), 6578–6589. https://doi.org/10.1002/mma.8926 doi: 10.1002/mma.8926
    [15] W. Liu, J. Huang, Q. Yao, Stability analysis for quaternion-valued inertial memristor-based neural networks with time delays, Neurocomputing, 448 (2021), 67–81. https://doi.org/10.1016/j.neucom.2021.03.106 doi: 10.1016/j.neucom.2021.03.106
    [16] Y. Shi, X. Chen, P. Zhu, Dissipativity for a class of quaternion-valued memristor-based neutral-type neural networks with time-varying delays, Math. Methods Appl. Sci., 46 (2023), 18166–18184. https://doi.org/10.1002/mma.9551 doi: 10.1002/mma.9551
    [17] T. Peng, J. Lu, Z. Tu, J. Lou, Finite-time stabilization of quaternion-valued neural networks with time delays: An implicit function method, Inf. Sci., 613 (2022), 747–762. https://doi.org/10.1016/j.ins.2022.09.014 doi: 10.1016/j.ins.2022.09.014
    [18] G. Tan, Z. Wang, Z. Shi, Proportional-integral state estimator for quaternion-valued neural networks with time-varying delays, IEEE Trans. Neural Networks Learn. Syst., 34 (2023), 1074–1079. https://doi.org/10.1109/TNNLS.2021.3103979 doi: 10.1109/TNNLS.2021.3103979
    [19] J. Hu, G. Tan, L. Liu, A new result on H$\infty$ state estimation for delayed neural networks based on an extended reciprocally convex inequality, IEEE Trans. Circuits Syst. II Express Briefs, 71 (2024), 1181–1185. https://doi.org/10.1109/TCSII.2023.3323834 doi: 10.1109/TCSII.2023.3323834
    [20] J. Cai, J. Feng, J. Wang, Y. Zhao, Quasi-synchronization of neural networks with diffusion effects via intermittent control of regional division, Neurocomputing, 409 (2020), 146–156. https://doi.org/10.1016/j.neucom.2020.05.037 doi: 10.1016/j.neucom.2020.05.037
    [21] R. Li, X. Gao, J. Cao, Quasi-state estimation and quasi-synchronization control of quaternion-valued fractional-order fuzzy memristive neural networks: Vector ordering approach, Appl. Math. Comput., 362 (2019), 124572. https://doi.org/10.1016/j.amc.2019.124572 doi: 10.1016/j.amc.2019.124572
    [22] X. Song, X. Li, S. Song, Y. Zhang, Z. Ning, Quasi-synchronization of coupled neural networks with reaction-diffusion terms driven by fractional brownian motion, J. Franklin Inst., 358 (2021), 2482–2499. https://doi.org/10.1016/j.jfranklin.2021.01.023 doi: 10.1016/j.jfranklin.2021.01.023
    [23] Z. Zhang, T. Zheng, S. Yu, Finite-time anti-synchronization of neural networks with time-varying delays via inequality skills, Neurocomputing, 356 (2019), 60–68. https://doi.org/10.1016/j.neucom.2019.05.012 doi: 10.1016/j.neucom.2019.05.012
    [24] X. Liu, Z. Li, Finite time anti-synchronization of complex-valued neural networks with bounded asynchronous time-varying delays, Neurocomputing, 387 (2020), 129–138. https://doi.org/10.1016/j.neucom.2020.01.035 doi: 10.1016/j.neucom.2020.01.035
    [25] Y. Qiao, H. Yan, L. Duan, J. Miao, Finite-time synchronization of fractional-order gene regulatory networks with time delay, Neural Networks, 126 (2020), 1–10. https://doi.org/10.1016/j.neunet.2020.02.004 doi: 10.1016/j.neunet.2020.02.004
    [26] T. Peng, J. Zhong, Z. Tu, J. Lu, J. Lou, Finite-time synchronization of quaternion-valued neural networks with delays: A switching control method without decomposition, Neural Networks, 148 (2022), 37–47. https://doi.org/10.1016/j.neunet.2021.12.012 doi: 10.1016/j.neunet.2021.12.012
    [27] T. Peng, J. Qiu, J. Lu, Z. Tu, J. Cao, Finite-time and fixed-time synchronization of quaternion-valued neural networks with/without mixed delays: An improved one-norm method, IEEE Trans. Neural Networks Learn. Syst., 12 (2022), 7475–7487. https://doi.org/10.1109/TNNLS.2021.3085253 doi: 10.1109/TNNLS.2021.3085253
    [28] D. Ding, X. Yao, H. Zhang, Complex projection synchronization of fractional-order complex-valued memristive neural networks with multiple delays, Neural Process. Lett., 51 (2020), 325–345. https://doi.org/10.1007/s11063-019-10093-x doi: 10.1007/s11063-019-10093-x
    [29] Y. Zhang, S. Deng, Finite-time projective synchronization of fractional-order complex-valued memristor-based neural networks with delay, Chaos, Solitons Fractals, 128 (2019), 176-190. https://doi.org/10.1016/j.chaos.2019.07.043 doi: 10.1016/j.chaos.2019.07.043
    [30] Y. Cheng, Y. Shi, The exponential synchronization and asymptotic synchronization of quaternion-valued memristor-based Cohen-Grossberg neural networks with time-varying delays, Neural Process. Lett., 55 (2023), 6637–6656. https://doi.org/10.1007/s11063-023-11152-0 doi: 10.1007/s11063-023-11152-0
    [31] Y. Cheng, Y. Shi, J. Guo, Exponential synchronization of quaternion-valued memristor-based Cohen-Grossberg neural networks with time-varying delays: Norm method, Cognit. Neurodyn., 18 (2024), 1943–1953. https://doi.org/10.1007/s11571-023-10057-x doi: 10.1007/s11571-023-10057-x
    [32] H. L. Li, L. Zhang, C. Hu, H. Jiang, J. Cao, Global Mittag-Leffler synchronization of fractional-order delayed quaternion-valued neural networks: Direct quaternion approach, Appl. Math. Comput., 373 (2020), 125020. https://doi.org/10.1016/j.amc.2019.125020 doi: 10.1016/j.amc.2019.125020
    [33] Y. Kao, Y. Li, J. H. Park, X. Chen, Mittag-Leffler synchronization of delayed fractional memristor neural networks via adaptive control, IEEE Trans. Neural Networks Learn. Syst., 32 (2020), 2279–2284. https://doi.org/10.1109/TNNLS.2020.2995718 doi: 10.1109/TNNLS.2020.2995718
    [34] J. Cheng, L. Xie, D. Zhang, H. Yan, Novel event-triggered protocol to sliding mode control for singular semi-Markov jump systems, Automatica, 151 (2023), 110906. https://doi.org/10.1016/j.automatica.2023.110906 doi: 10.1016/j.automatica.2023.110906
    [35] J. Cheng, J. H. Park, Z. Wu, Observer-based asynchronous control of nonlinear systems with dynamic event-based try-once-discard protocol, IEEE Trans. Cybern., 52 (2022), 12638–12648. https://doi.org/10.1109/TCYB.2021.3104806 doi: 10.1109/TCYB.2021.3104806
    [36] Y. Gu, Y. Yu, H. Wang, Projective synchronization for fractional-order memristor-based neural networks with time delays, Neural Comput. Appl., 31 (2019), 6039–6054. https://doi.org/10.1007/s00521-018-3391-7 doi: 10.1007/s00521-018-3391-7
    [37] G. Velmurugan, R. Rakkiyappan, Hybrid projective synchronization of fractional-order memristor-based neural networks with time delays, Nonlinear Dyn., 83 (2016), 419–432. https://doi.org/10.1007/s11071-015-2337-1 doi: 10.1007/s11071-015-2337-1
    [38] H. Bao, J. Cao, Projective synchronization of fractional-order memristor-based neural networks, Neural Networks, 63 (2015), 1–9. https://doi.org/10.1016/j.neunet.2014.10.007 doi: 10.1016/j.neunet.2014.10.007
    [39] R. Li, X. Gao, J. Cao, K. Zhang, Exponential stabilization control of delayed quaternion-valued memristive neural networks: Vector ordering approach, Circuits Syst. Signal Process., 39 (2020), 1353–1371. https://doi.org10.1007/s00034-019-01225-8 doi: 10.1007/s00034-019-01225-8
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(375) PDF downloads(30) Cited by(0)

Article outline

Figures and Tables

Figures(8)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog