一、 个人简介

硕士生导师，天津工业与应用数学会常务理事，天津数学会理事，二级学科带头人，学院微分方程团队负责人，学院应用数学学科带头人，学院教学督导组组长和方程教研室主任。研究领域为神经网络理论及应用，首次提出比例时滞神经网络模型，并应用非线性变换法研究比例时滞神经网络的动力学，突破了有界时滞神经网络理论的研究方法。首次提出了比例时滞神经网络多项式稳定性概念，得到国内外学者广泛关注及研究，为比例时滞神经网络动力学研究做出重要贡献。近年来成果发表在控制等本学科领域的重要学术期刊IEEE Transactions on Systems, Man and Cybernetics: Systems、IEEE Transactions on Network Science and Engineering、Information Sciences、Communications in Nonlinear Science and Numerical Simulation、Nonlinear Dnynamics 和 ISA Transactions。作为第二主研人参与完成国家自然科学基金2项，主持完成天津市自然科学基金面上项目1项，主持完成天津市教委项目1项，主持完成天津市教改项目1项。出版专著一部。

个人经历

学习经历

时间	单位	职称/职务/学历学位
2004/9-2007/9	哈尔滨工业大学航天学院控制科学与控制工程系控制科学与控制工程专业	工学博士
2002/9-2004/7	哈尔滨工业大学理学院数学系基础数学专业	理学硕士
1994/9-1998/7	齐齐哈尔大学数学系	理学硕士

二、 工作经历

时间	单位	职称/职务/学历学位
2014/11-至今	欧冠足球官网地址	教授
2008/10-2014/10	欧冠足球官网地址	副教授
2006/7-2008/9	欧冠足球官网地址	讲师
1998/7-2006/6	齐齐哈尔大学理学院数学系	助教 讲师

三、承担课程

   为本科生讲授《数学分析3-3》，《常微分方程》，《复变函数》，和《数学物理方程》等专业基础课程和通识课《当代数学前沿》。

  为研究生讲授偏微分方程，神经网络稳定性理论等专业课程。

六、  成果

1. 科研项目

[1] 2018/10-2021/6，主持天津市自然科学基金项目《具比例时滞复杂神经网络的动力学行为与仿真研究》，（No. JCYBJC85800），已结题。

[2] 2014/1-2018/12，参与国家自然科学基金项目《基于积分模度量的折线模糊神经网络与广义模糊系统的逼近分析及图像处理》（No. 61374009），第二主研人，已结题。

[3] 2009/1-2013/12, 参与国家自然科学基金项目《基于集值模糊积分的随机延时细胞神经网络的应用研究》（No.60974144）， 第二主研人，已结题。

[4] 2010/1-2013/5，主持天津市高校科技发展基金计划项目《基于人工神经网络的多约束QoS路由算法研究》（No.20100813），已结题。

[5] 2009/12-2012/9，主持欧冠足球官网地址博士基金项目《延时细胞神经网络的稳定性研究与仿真》(No.52LX34)，已结题。

2. 专著

[1] 周立群，具比例时滞递归神经网络的稳定性及其仿真与应用，北京：机械工业出版社，2019.1

3. 论文发表

[1] Liqun Zhou, Zhixue Zhao, Quanxin Zhu* , Rui Zhou, Tingwen Huang. Global polynomial stabilization of impulsive neural networks with bidirectional proportional delays.  IEEE Transactions on Network Science and Engineering, 2024: 11(1): 471-484.

[2]  Shuyi Jia, Liqun Zhou*. Fixed-time stabilization of fuzzy neutral-type inertial neural networks with proportional delays, ISA Transactions, 2024, 144: 167-175.

[3] Liqun Zhou*, Zhixue Zhao.  Delay-dependent passivity of impulsive coupled reaction-diffusion neural networks with multi-proportional delays. Communications in Nonlinear Science and Numerical Simulation,  2023, 126: 107415:1-107415:20.

[4] Liqun Zhou, Quanxin Zhu*, Tingwen Huang. Global polynomial synchronization of proportional delayed inertial neural networks. IEEE Transactions on Systems, Man and Cybernetics: Systems, 2023, 53(7): 4487-4497.

[5] Qian Li, Liqun Zhou*. Global asymptotic synchronization of inertial memristive Cohen-Grossberg neural networks with proportional delays. Communications in Nonlinear Science and Numerical Simulation,  2023, 123: 107295:1-107295:15.

[6] Qian Li, Liqun Zhou*. Global polynomial stabilization of proportional delayed inertial memristive neural networks. Information Sciences, 2023, 623: 729-747.

[7] 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.

[8] Liqun Zhou*, Zhixue Zhao. Global polynomial periodicity and polynomial stability of Cohen-Grossberg neural networks with proportional delays. ISA Transactions, 2022, 122: 205-217.

[9] Yongkang Zhang, Liqun Zhou*. Stabilization and lag synchronization of proportional delayed impulsive complex-valued inertial neural networks. Neurocomputing, 2022, 507: 428-440.

[10]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.

[11] Liqun Zhou*. Global exponential dissipativity of impulsive recurrent neural networks with multi-proportional delays. Neural Processing Letters, 2021, 53: 1435-1452.

[12]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.

[13] Zhou Liqun*, Zhao Zhixue. Exponential synchronization and polynomial synchronization of recurrent neural networks with and without proportional delays. Neurocomputing, 2020, 372(1): 109-116.

[14] 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.

[15] Lin Xing, Liqun Zhou*. Polynomial dissipativity of proportional delayed BAM neural networks. International Journal of Biomathematics, 2020, 13(6): 2050050:1-2050050:20.

[16] 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.

[17] 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.

[18] 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.

[19] Lijuan Su, Liqun Zhou*. Psaaivity of memristor-based recurrent neural networks with multi-proportional delay. Neurocomputing, 2017, 266: 485-493.

[20] 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.

[21] 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.

[22] Liqun Zhou*, Yanyan Zhang. Global exponential periodicity and stability of recurrent neural networks with multi-proportional delays. ISA Transactions, 2016, 60: 89-95.

[23]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.

[24] 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.

[25] Liqun Zhou*. Delay-dependent exponential synchronization of recurrent neural networks with multiple proportional delays. Neural Processing Letters, 2015, 42(3): 619-632.

[26] 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.

[27] Liqun Zhou*. Novel global exponential stability criteria for hybrid BAM neural networks with proportional delays. Neurocomputing, 2015, 161: 99-106.

[28] Liqun Zhou*. Global asymptotic stability of cellular neural networks with proportional delays. Nonlinear Dynamics, 2014, 77(1): 41-47.

[29] Liqun Zhou*, Xiubo hen, Yixian Yang. Asymptotic stability of cellular neural networks with multi-proportional delays. Applied Mathematics and Computation, 2014, 229: 457-466.

[30] Liqun Zhou*. Dissipativity of a class of cellular neural networks with proportional delays. Nonlinear Dynamics, 2013, 73(3): 1895-1903.

[31] Liqun Zhou*. Delay-dependent exponential stability of cellular neural networks with multi-proportional delays. Neural Processing Letters, 2013, 38(3): 347-359.

[32] 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.

[33] 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.

[34] 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.