郑明文

作者:
来源:数学与统计学院
发布时间:2021-07-25
阅览次数:1765


 

数学与统计学院导师基本信息

  

郑明文

性别

出生年月

1980.5

学历/学位

博士研究生

导师类别博导/硕导

硕导

职称/职务

副教授/信科系主任

电子邮箱

sdlgzmw@sdut.edu.cn

研究方向

神经网络稳定性与控制、忆阻器模型、非线性动力学、压缩感知(CS)、回声状态网络(ESN

代表性

论著

发表SCI学术论文40余篇,部分论文如下所示:

1Fan Yang, Wen Wang,Lixiang Li, Mingwen Zheng(通讯作者), Yanping Zhang, Zhenying Liang. Finite-time parameter identification of fractional-order time-varying delay neural networks based on synchronization [J]. Chaos: An Interdisciplinary Journal of Nonlinear Science, 2023, 33(3): 033146.

2Zijian Wang, Hui Zhao, Mingwen Zheng(通讯作者), Sijie Niu, Xizhan Gao, Lixiang Li, A novel time series prediction method based on pooling compressed sensing echo state network and its application in stock market [J], Neural Networks, Volume 164,2023, Pages 216-227

3Xilong Qu, Yanping  Zhang, Yanxin Wei, Zhengjun Wei, Mingwen Zheng(通讯作者). Finitetime parameter identification of fractionalorder uncertain coupling recurrent neural networks based on synchronization[J]. Mathematical Methods in the Applied Sciences, 2021.

4Hao Zhang, Mingwen Zheng(通讯作者), Yanping Zhang, Xiao Yu, Wenchao Li, Hui Gao. Application of ESN Prediction Model Based on Compressed Sensing in Stock Market [J]. Communications in Nonlinear Science and Numerical Simulation, 2021101: 105857.

5Zhongfeng Niu, Mingwen Zheng(通讯作者), Yanping Zhang, Tianzhen Wang. A New Asymmetrical Encryption Algorithm based on Semi-Tensor Compressed Sensing in WBANs [J]. IEEE Internet of Things Journal, 2020, 1(7):734-750.

6Peifei Guo, Zhenying Liang, Xi Wang, Mingwen Zheng. Adaptive trajectory tracking of wheeled mobile robot based on fixed-time convergence with uncalibrated camera parameters [J]. ISA transactions, 2020, 99: 1-8.

7Zengke Jin, Zhenying Liang, Peifei Guo, Mingwen Zheng. Adaptive backstepping tracking control of a car with n trailers based on RBF neural network[J]. Asian Journal of Control, 2021, 23(2): 824-834.

8Zengke Jin, Zhenying Liang, Xi Wang, Mingwen Zheng. Adaptive Backstepping Sliding Mode Control of Tractor-trailer System with Input Delay Based on RBF Neural Network [J]. International Journal of Control, Automation and Systems, 2021, 19(1): 76-87.

9Mingwen Zheng, Lixiang Li,Haipeng Peng, Jinghua Xiao,Yixian Yang, Yanping Zhang, Hui Zhao. General decay synchronization of complex multi-links time-varying dynamic network [J]. Communications in Nonlinear Science and Numerical Simulation, 2019, 67: 108-123.

10Rong X, Jiang D, Zheng M, et al. Meaningful data encryption scheme based on newly designed chaotic map and P-tensor product compressive sensing in WBANs [J]. Nonlinear Dynamics, 2022: 1-17.

11Xin HuJiang DonghuaJiang DonghuaMusheer AhmadMusheer AhmadShow, Tsafacck Nestor, Liya Zhu, Mingwen Zheng, Novel 3-D hyperchaotic map with hidden attractor and its application in meaningful image encryption [J], Nonlinear Dynamics, 2023,  DOI: 10.1007/s11071-023-08545-0.

12Mingwen Zheng, Zeming Wang , Lixiang Li*, Haipeng Peng, Jinghua Xiao, Yixian Yang, Yanping Zhang, Cuicui Feng. Finite-time generalized projective lag synchronization criteria for neutral-type neural networks with delay [J]. Chaos, Solitons & Fractals, 2018,107, 195–203

13Mingwen Zheng, Lixiang Li*, Haipeng Peng, Jinghua Xiao, Yixian Yang, Yanping Zhang, Hui Zhao. Finite-time stability and synchronization of memristor-based fractional-order fuzzy cellular neural networks [J].Communications in Nonlinear Science and Numerical Simulation, 59 (2018) 272–291.

14Mingwen Zheng, Lixiang Li*, Haipeng Peng, Jinghua Xiao, Yixian Yang, Hui Zhao. Finite-time projective synchronization of memristor-based delay fractional-order neural networks [J]. Nonlinear Dynamics, 2017, 89(4):2641-2655.

15Mingwen Zheng, Lixiang Li*, Haipeng Peng, Jinghua Xiao, Yixian Yang, Hui Zhao. Parameters estimation and synchronization of uncertain coupling recurrent dynamical neural networks with time-varying delays based on adaptive control [J]. Neural Computing and Applications 30.7 (2018): 2217-2227.

16Mingwen Zheng, Lixiang Li*, Haipeng Peng, Jinghua Xiao, Yixian Yang, Hui Zhao. Finite-time stability analysis for neutral-type neural networks with hybrid time-varying delays without using Lyapunov method [J]. Neurocomputing, 2017, 238(C):67-75.

17Yanping Zhang, Lixiang Li,Haipeng Peng, Jinghua Xiao,Yixian Yang, Mingwen Zheng(通讯作者), Hui Zhao. Finitetime synchronization for memristorbased BAM neural networks with stochastic perturbations and timevarying delays[J]. International Journal of Robust and Nonlinear Control, 2018, 28(16): 5118-5139.

18Mingwen Zheng, Lixiang Li,Haipeng Peng, Jinghua Xiao,Yixian Yang, Yanping Zhang, Hui Zhao. Fixed-time synchronization of memristor-based fuzzy cellular neural network with time-varying delay [J]. Journal of the Franklin Institute, 2018, 355(14): 6780-6809.

19Mingwen Zheng, Lixiang Li*, Haipeng Peng, Jinghua Xiao, Yixian Yang, Hui Zhao. Finite-time stability and synchronization for memristor-based fractional-order Cohen-Grossberg neural network [J]. European Physical Journal B, 2016, 89(9):204.

20Mingwen Zheng, Lixiang Li*, Haipeng Peng, Jinghua Xiao, Yixian Yang, Hui Zhao,Jingfeng Ren. Finite-time synchronization of complex dynamical networks with multi-links via intermittent controls [J]. European Physical Journal B, 2016, 89(2):1-12.

21Zhao H, Li L, Peng H, et al. Impulsive control for synchronization and parameters identification of uncertain multi-links complex network[J]. Nonlinear Dynamics, 2016, 83(3): 1437-1451.

科研项目

主持博士创新基金、企业横向课题、军工项目各一项,参与国家重点研发计划项目1项,国家自然科学基金面上项目1项,参与山东省自然科学基金面上项目2项。

1、军工项目-前沿科技创新专项:基于XXX工具研究,2021.12-2022.12,主持

2、国家自然科学基金面上项目:三维高频无限声场模拟的边界元法研究12172201,2022.1-2025.12,7排2

3、山东省自然科学基金面上项目:复杂不确定机器人链式时滞系统的固定时间轨迹跟踪控制,ZR2021MF072,2022.1-2024.12,7排3

4、山东省自然科学基金面上项目:基于Arnold 随机动力系统理论的梁/薄板动力学全局分析,ZR2020MA054,2021.1-2023.12,7排3

5、国家重点研发计划子课题:区块链系统脆弱性发现和利用技术2020YFB1005704,6排4

6、山东理工大学科技博士启动基金 418048,主持

7、山东省自然科学基金面上项目:复杂分数阶忆阻神经网络模型构建、控制及应用研究,ZR2023MF0362024.1.1-2026.12.31,主持

荣誉称号

2020年山东理工大学优秀教师

学术兼职

担任多个SCI期刊审稿人,包括ND, CNSNS, TCB, TNNLS, TII, FI, CSF, NCA, Physica A, ITETCI, SMC, MMA, ISA Trans., FSS, IEEE TCDS,  SMCA, EPL, ESWA等。

人才培养

 欢迎喜欢从事科学研究工作的同学报考。

 


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