[1] Liqun Zhou, Quanxin Zhu*, Tingwen Huang, Global polynomial synchronization of proportional delayed inertial neural networks. IEEE Transactions on Systems, Man and Cybernetics: Systems, to be published, 2023, doi:10.1109/TSMC.2023.3249664.
[2] Qian Li, Liqun Zhou*. Global asymptotic synchronization of inertial memristive Cohen-Grossberg neural networks with proportional delays. Communications in Nonlinear Science and Numerical Simulation, to be published, 2023, doi.org/10.1016/j.cnsns.2023.107295.
[3] Qian Li, Liqun Zhou*. Global polynomial stabilization of proportional delayed inertial memristive neural networks. Information Sciences, 2023, 623:729-747.
[4] Xiehui Song, Liqun Zhou*, Yu Wang, Shiru Zhang, Yuji Zhang. Stability analysis of proportional delayed projection neural network for quadratic programming problem. International Journal of Biomathematics, 2023, 16(1):2250070-1:2250070-25.
[5] Liqun Zhou*, Zhixue Zhao. Global polynomial periodicity and polynomial stability of Cohen-Grossberg neural networks with proportional delays. ISA Transactions, 2022, 122:205-217.
[6] Yongkang Zhang, Liqun Zhou*. Stabilization and lag synchronization of proportional delayed impulsive complex-valued inertial neural networks. Neurocomputing, 2022, 507:428-440.
[7]Yongkang Zhang, Liqun Zhou*. Novel global polynomial stability criteria of impulsive complex-valued neural networks with multi-proportional delays. Neural Computing and Applications, 2022, 34:2913-2924.
[8] Liqun Zhou*. Global exponential dissipativity of impulsive recurrent neural networks with multi-proportional delays. Neural Processing Letters, 2021, 53:1435-1452.
[9]Liqun Zhou*, Zhixue Zhao. Asymptotic stability and polynomial stability of impulsive Cohen-Grossberg neural networks with multi-proportional delays. Neural Processing Letters, 2020, 51(2): 2607-2627.
[10] Zhou Liqun*, Zhao Zhixue. Exponential synchronization and polynomial synchronization of recurrent neural networks with and without proportional delays. Neurocomputing, 2020, 372(1): 109-116.
[11] Rui Zhou, Liqun Zhou*. Global polynomial stabilization and global asymptotic stabilization of coupled neural networks with multi-proportional delays. Mathematical Methods in the Applied Sciences, 2020, 43(12): 7345-7360.
[12] Lin Xing, Liqun Zhou*. Polynomial dissipativity of proportional delayed BAM neural networks. International Journal of Biomathematics, 2020, 13(6):2050050:1-2050050:20.
[13] Liqun Zhou*, Zhixue Zhao. Global asymptotic periodicity of impulsive Cohen-Grossberg neural networks with multi-proportional delays. Proceedings of the 39th Chinese Control Conference, July 27-29, 2020, Shenyang, China.
[14] Lijuan Su, Liqun Zhou*. Exponential synchronization of memristor-based recurrent neural networks with multi-proportional delays. Neural Computing and Applications, 2019, 31(11): 7907-7920.
[15] Liqun Zhou*, Delay-dependent and independent passivity of a class of recurrent neural networks with impulse and multi-proportional delays. Neurocomputing, 2018, 308: 235-244.
[16] Lijuan Su, Liqun Zhou*. Psaaivity of memristor-based recurrent neural networks with multi-proportional delay. Neurocomputing, 2017, 266: 485-493.
[17] Liqun Zhou*, Xueting Liu. Mean-square exponential input-to-state stability of stochastic recurrent neural networks with multi-proportional delays. Neurocomputing, 2017, 219:396-403.
[18] Liqun Zhou*. Delay-dependent exponential stability of recurrent neural networks with Markovian jumping parameters and proportional delay. Neural Computing and Application, 2017, 28(s1): 765-773.
[19] Liqun Zhou*, Yanyan Zhang. Global exponential periodicity and stability of recurrent neural networks with multi-proportional delays. ISA Transactions, 2016, 60: 89-95.
[20]Liqun Zhou*, Yanyan Zhang. Global exponential stability of a class of impulsive recurrent neural networks with proportional delays via fixed point theory. Journal of the Franklin Institute, 2016, 353(2): 561-575.
[21] Liqun Zhou*, Zhongying Zhao. Exponential stability of a class of competitive neural networks with multi-proportional delays. Neural Processing Letters, 2016, 44(3): 651-663.
[22] Liqun Zhou*. Delay-dependent exponential synchronization of recurrent neural networks with multiple proportional delays. Neural Processing Letters, 2015, 42(3): 619-632.
[23] Liqun Zhou*, Yanyan Zhang. Global exponential stability of cellular neural networks with multi-proportional delays. International Journal of Biomathematics, 2015, 8(6): 1550071:1-1550071:17.
[24] Liqun Zhou*. Novel global exponential stability criteria for hybrid BAM neural networks with proportional delays. Neurocomputing, 2015, 161: 99-106.
[25] Liqun Zhou*. Global asymptotic stability of cellular neural networks with proportional delays. Nonlinear Dynamics, 2014, 77(1): 41-47.
[26] Liqun Zhou*, Xiubo hen, Yixian Yang. Asymptotic stability of cellular neural networks with multi-proportional delays. Applied Mathematics and Computation, 2014, 229: 457-466.
[27] Liqun Zhou*. Dissipativity of a class of cellular neural networks with proportional delays. Nonlinear Dynamics, 2013, 73(3): 1895-1903.
[28] Liqun Zhou*. Delay-dependent exponential stability of cellular neural networks with multi-proportional delays. Neural Processing Letters, 2013, 38(3): 347-359.
[29] Liqun Zhou*. On the global dissipativity of a class of cellular neural networks with multi-pantograph delays. Advances in Artificial Neural Systems, 2011, 2011: 941426:1-941426:7.
[30] Liqun Zhou*, Guangda Hu. Global exponential periodicity and stability of cellular neural networks with variable and distributed delays. Applied Mathematics and Computation, 2008, 195(2): 402-411.
[31] Liqun Zhou*, Guangda Hu. Almost sure exponential stability of neural stochastic delayed cellular neural networks. Journal of Control Theory and Application, 2008, 6(2):195-200.