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Keywords:
prescribed-time synchronization; event-triggered control; pinning control; memristive neural networks; Zeno behavior
Summary:
This paper investigates the issues of prescribed-time synchronization for memristive neural networks with time-varying delay by event-triggered pinning control. To conserve resources and enhance control efficiency, two event-based control schemes and the measurement error function are obtained. Then, using Lyapunov stability theory and inequality techniques, some sufficient conditions are obtained to ensure the prescribed time synchronization of the response system and the drive system. Furthermore, under the two event trigger conditions, a positive lower bound on the inter-event time is derived respectively to ensure that Zeno behavior can be excluded during the whole time span except the prescribed settling time. Finally, numerical simulations are provided to illustrate the effectiveness of the obtained theoretical results.
References:
[1] Ai, W., Zhai, J., Fei, S.: Global finite-time stabilization for a class of stochastic nonlinear systems by dynamic state feedback. Kybernetika 49 (2013), 590-600. DOI 
[2] Chen, X., Yu, H., Hao, F.: Prescribed-time event-triggered bipartite consensus of multiagent systems. IEEE Trans. Cybern. 52 (2022), 2589-2598. DOI 
[3] Chua, L.: Memristor-the missing circuit element. IEEE Trans. Circuit Theory 18 (1971), 507-519. DOI 
[4] Dong, M., Wen, S., Zeng, Z., Yan, Z., Huang, T.: Sparse fully convolutional network for face labeling. Neurocomputing 331 (2019), 465-472. DOI 
[5] Fan, Y., Huang, X., Shen, H., Cao, J.: Switching event-triggered control for global stabilization of delayed memristive neural networks: An exponential attenuation scheme. Neural Netw. 117 (2019), 216-224. DOI 
[6] Gong, S., Guo, Z., Wen, S., Huang, T.: Finite-time and fixed-time synchronization of coupled memristive neural networks with time delay. IEEE Trans. Cybern. 51 (2021), 2944-2955. DOI 
[7] Guo, Z., Yang, S., Wang, J.: Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control. Neural Netw. 84 (2016), 67-69. DOI 
[8] Jia, X., Li, H., Chi, X.: Prescribed-time consensus of integrator-type multi-agent systems via sampled-data control. IEEE Trans. Circuits Syst. II Exp. Briefs 71 (2024), 3413-3417. DOI 
[9] Liu, H., Lu, J. A., Lu, J., Hill, D. J.: Structure identification of uncertain general ¨ complex dynamical networks with time delay. Automatica 45 (2009), 1799-1807. DOI 
[10] Lyu, D., Sun, M., Jia, Q.: Event-based prescribed-time synchronization of directed dynamical networks with lipschitzian nodal dynamics. IEEE Trans. Circuits Syst. II Exp. Briefs 69 (2021), 1847-1851. DOI 
[11] Pershin, Y., Ventra, M. D.: Experimental demonstration of associative memory with memristive neural networks. Neural Netw. 23 (2010), 881-886. DOI 
[12] Strukov, D. B., Snider, G. S., Stewart, D. R., Williams, R. S.: The missing memristor found. Nature 453 (2008), 80-83. DOI 
[13] Wang, H., Duan, S., Huang, T., Tan, J.: Synchronization of memristive delayed neural networks via hybrid impulsive control. Neurocomputing 267 (2017), 615-623. DOI 
[14] Wang, K., Teng, Z., Jiang, H.: Adaptive synchronization of neural networks with time-varying delay and distributed delay. Physica A 387 (2008), 631-642. DOI 
[15] Wang, S., Jian, J.: Predefined-time synchronization of fractional-order memristive competitive neural networks with time-varying delays. Chaos Solitons Fractals 174 (2023), 113790. DOI 
[16] Peng, Z., Zhang, Z., Luo, R., Kuang, Y., Hu, J., Cheng, H., Ghosh, B. K.: Event-triggered optimal control of completely unknown nonlinear systems via identifier-critic learning. Kybernetika 59 (2023), 365-391. DOI 
[17] Wang, X., Park, J. H., Liu, Z., Yang, H.: Dynamic event-triggered control for GSES of memristive neural networks under multiple cyber-attacks. IEEE Trans. Neural Netw. Learn. Syst. 35 (2024), 7602-7611. DOI 
[18] Wang, Y., Song, Y., Hill, D. J., M.Krstic: Prescribed-time consensus and containment control of networked multiagent systems. IEEE Trans. Cybern. 49 (2018), 1138-1147. DOI 
[19] Wu, Q., Zhou, J., Lan, X.: Impulses-induced exponential stability in recurrent delayed neural networks. Neurocomputing 74 {(2011)}, 3204-3211. DOI 
[20] Xu, W., Zhu, S., Fang, X., Wang, W.: Adaptive anti-synchronization of memristor-based complex-valued neural networks with time delays. Phys A 535 (2019), 122427. DOI 
[21] Yan, Z., Liu, W., Wen, S., Yang, Y.: Multi-label image classification by feature attention network. IEEE Access 7 (2019), 98005-98013. DOI 
[22] Yan, L., Wang, Z., Zhang, Y., Fan, Y.: Sampled-data control for mean-square exponential stabilization of memristive neural networks under deception attacks. Chaos Solitons Fractals 174 (2023), 113787. DOI 
[23] Yang, J., Chen, G., Zhu, S., Wen, S., Hu, J.: Fixed/prescribed-time synchronization of BAM memristive neural networks with time-varying delays via convex analysis. Neural Netw. 163 (2023), 53-63. DOI 
[24] Yang, S., Li, C., Huang, T.: Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control. Neural Netw. 75 (2016), 162-172. DOI 
[25] Yang, Z., Luo, B., Liu, D., Li, Y.: Pinning synchronization of memristor-based neural networks with time-varying delays. Neural Netw. 93 (2017), 143-151. DOI 
[26] Zhang, H., Liu, Z., Huang, G. B., Wang, Z.: Novel weighting-delay based stability criteria for recurrent neural networks with time-varying delay. IEEE Trans. Neur. Netw. 21 (2009), 91-106. DOI 
[27] Zhou, J., Wu, Q.: Exponential stability of impulsive delayed linear differential equations. IEEE Trans. Circuits Syst. II, Exp. Briefs 56 (2009), 744-48. DOI 
[28] Zhou, J., Wu, Q., Xiang, L.: Pinning complex delayed dynamical networks by a single impulsive controller. IEEE Trans. Circuits Syst. I Regul. Pap. 58 (2011), 2882-2893. DOI 
[29] Zhu, W., Jiang, Z. P.: Event-based leader-following consensus of multi-agent systems with input time delay. IEEE Trans. Autom. Control 60 (2014), 1362-1367. DOI 
[30] Zhu, Y., Jiang, M.: Prescribed-time synchronization of inertial memristive neural networks with time-varying delays. AIMS Math. 10 (2025), 9900-9916. DOI 
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