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张瑞梅

日期:2021-01-25                   来源:                   作者:               关注:

专业 研究方向
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张瑞梅

专业:应用数学

研究方向:动力系统与控制

Email: ruimeizhang163@163.com


个人简介:

201912月在电子科技大学获理学博士学位,20179月—20188在加拿大University of Waterloo访学,201810月—201910月在韩国Yeungnam University访学,20203月作为引进人才加入网投平台哪个信誉更高。入选网投平台哪个信誉更高“双百人才工程B计划”。

一直从事反应扩散神经网络、忆阻神经网络、复杂网络、采样控制和时滞系统的研究,发表学术论文30余篇。以第一作者/通讯作者身份在IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, Fuzzy Sets and Systems, Information Sciences等国际期刊上发表SCI论文26篇,其中包括10IEEE Transactions系列汇刊论文,4ESI高被引论文,论文总被引用600余次(Google学术统计)。主持国家自然科学基金1项。现担任国家自然科学基金项目通讯评审专家,美国《数学评论》评论员和10余个国际SCI期刊的审稿人。

项目资助:

[1] 国家自然科学基金青年项目,62003229,反应扩散神经网络系统的优化采样控制问题研究,2021/01-2023/12主持

代表性学术论文:

[1] R. Zhang, D. Zeng, J.H. Park*, H.K. Lam, X. Xie, Fuzzy sampled-data control for synchronization of T-S fuzzy reaction-diffusion neural networks with additive time-varying delays, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2020.2996619.

[2] R. Zhang, D. Zeng, J.H. Park, Y. Liu, X. Xie, Adaptive event-triggered synchronization of reaction-diffusion neural networks, IEEE Transactions on Neural Networks and Learning Systems, DOI:10.1109/TNNLS.2020.3027284.

[3] R. Zhang, D. Zeng, J.H. Park*, H.K. Lam*, S. Zhong, Fuzzy adaptive event-triggered sampled-data control for stabilization of T-S fuzzy memristive neural networks with reaction-diffusion terms, IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2020.2985334.

[4] R. Zhang, D. Zeng, J.H. Park*, Y. Liu, S. Zhong, A new approach to stabilization of chaotic systems with non-fragile fuzzy proportional retarded sampled-data control, IEEE Transactions on Cybernetics, vol. 49, no. 9, pp. 3218-3229, 2019.

[5] R. Zhang, D. Zeng, J.H. Park*, Y. Liu, S. Zhong, A new approach to stochastic stability of Markovian neural networks with generalized transition rates, IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 2, pp. 499-510, 2019.

[6] R. Zhang, D. Zeng, J.H. Park*, Y. Liu, S. Zhong, Pinning event-triggered sampling control for synchronization of T-S fuzzy complex networks with partial and discrete-time couplings, IEEE Transactions on Fuzzy Systems, vol. 27, no. 12, pp. 2368-2380, 2019.

[7] R. Zhang, D. Zeng, X. Liu*, S. Zhong, J. Cheng, New results on stability analysis for delayed Markovian generalized neural networks with partly unknown transition rates, IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3384-3395, 2019.

[8] R. Zhang, D. Zeng, J.H. Park*, Y. Liu, S. Zhong, Quantized sampled-data control for synchronization of inertial neural networks with heterogeneous time-varying delays, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 12, pp. 6385-6395, 2018.

[9] R. Zhang, D. Zeng, J.H. Park*, Y. Liu, S. Zhong, Nonfragile sampled-data synchronization for delayed complex dynamical networks with randomly occurring controller gain fluctuations, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 12, pp. 2271-2281, 2018.  

[10] D. Zeng, R. Zhang*, J.H. Park*, Z. Pu, Y. Liu, Pinning synchronization of directed coupled reaction-diffusion neural networks with sampled-data communications, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 6, pp. 2092-2103, 2020.

[11] R. Zhang*, X. Liu, D. Zeng, S. Zhong, K. Shi, A novel approach to stability and stabilization of fuzzy sampled-data Markovian chaotic systems, Fuzzy Sets and Systems, vol. 344, pp. 108-128, 2018. ESI高被引论文

[12] R. Zhang, D. Zeng*, S. Zhong, Y. Yu, Event-triggered sampling control for stability and stabilization of memristive neural networks with communication delays, Applied Mathematics and Computation, vol. 310, pp. 57-74, 2017.ESI高被引论文

[13] R. Zhang, D. Zeng*, S. Zhong, K. Shi, J. Cui, New approach on designing stochastic sampled-data controller for exponential synchronization of chaotic Lur’e systems, Nonlinear Analysis: Hybrid Systems, vol. 29, pp. 303-321, 2018.ESI高被引论文

[14] R. Zhang*, D. Zeng, S. Zhong, Novel master-slave synchronization criteria of chaotic Lur’e systems with time delays using sampled-data control, Journal of the Franklin Institute, vol. 354, no. 12, pp. 4930-4954, 2017.ESI高被引论文

[15] R. Zhang, J.H. Park*, D. Zeng, Y. Liu, S. Zhong, A new method for exponential synchronization of memristive recurrent neural networks, Information Sciences, vol. 466, pp. 152-169, 2018.

[16] R. Zhang, D. Zeng*, X. Liu, S. Zhong, K. Shi, A new method for quantized sampled-data synchronization of delayed chaotic Lur’e systems, Applied Mathematical Modelling, vol. 70, pp. 471-489, 2019.

[17] R. Zhang, D. Zeng*, J.H. Park*, S. Zhong, Y. Yu, Novel discontinuous control for exponential synchronization of memristive recurrent neural networks with heterogeneous time-varying delays, Journal of the Franklin Institute, vol. 355, pp. 2826-2848, 2018.

[18] R. Zhang, D. Zeng, J.H. Park*, S. Zhong, Y. Liu, X. Zhou, New approaches to stability analysis for time-varying delay systems, Journal of the Franklin Institute, vol. 356, pp. 4174-4189, 2019.

[19] R. Zhang*, D. Zeng, X. Liu, S. Zhong, Q. Zhong, Improved results on state feedback stabilization for a networked control system with additive time-varying delay components' controller, ISA Transactions, vol. 75, pp. 1-14, 2018 .

[20] R. Zhang, D. Zeng*, J.H. Park*, K. Shi, Y. Liu, Stabilizability of complex complex-valued memristive neural networks using non-fragile sampled-data control, Journal of the Franklin Institute, DOI: https://doi.org/10.1016/j.jfranklin.2021.01.017.


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