东北大学导师教师师资介绍简介-王占山

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姓名：王占山
工作单位：信息科学与工程学院
职称：教授
学历：工学博士
出生年月： 1971.11
电话： +86-
电子邮箱： wangzhanshan@ise.neu.edu.cn; zhanshan_wang@163.com
MSN：

王占山，1971年11月生，博士，教授，博士生导师。1994年本科毕业包头钢铁学院（即内蒙古科技大学）工业电气自动化专业、2001年硕士毕业于抚顺石油学院（即辽宁石油化工大学）控制理论与控制工程专业、2006年博士毕业于东北大学控制理论与控制工程专业。自2000年以来，一直从事故障诊断和容错控制、神经网络稳定性和优化控制、自动控制理论等方面的研究，在IEEE Transactions系列会刊上合作发表论文20余篇，特别是自2005年以来在时滞递归神经网络稳定性理论方面提出了一系列基于矩阵不等式的分析方法。1篇合作论文获得2012年度IEEE Transactions on Neural Networks and Learning Systems优秀论文奖(outstanding award paper)。获得授权发明专利9项。以第四完成人获国家科技进步奖二等奖1项；以第三完成人获得辽宁省自然科学奖一等奖1项；以第五完成人获得教育部自然科学奖一等奖1项；出版中文专著2部，合作译著1部。
主持国家自然科学基金面上项目2项、中央高校基本科研业务费3项、中国博士后科学基金、中国博士后科学基金特别资助基金、教育部高等学校博士点专项基金(新教师基金)各1项；作为项目执行负责人参加国家自然科学基金重点基金2项、国家自然科学基金面上项目1项、教育部重点实验室开放基金1项，以及多项企事业横向课题等。
博士学位论文获得2007年东北大学优秀博士学位论文、2008年辽宁省优秀博士学位论文、2009年全国百篇优秀博士学位论文提名；2010年入选教育部新世纪优秀人才支持计划；2010年获得辽宁省优秀博士后称号；研究生成长历程被写入全国发行的《中国研究生》杂志，并作为2010年第10 期的封面人物进行重点报道；2013年入选辽宁省“百千万人才工程”百人层次人选。
社会兼职情况：
IEEE Transactions on Neural Networks and Learning Systems编委(2014年1月-2014年12月)，
自动化学报编委（2014年1月至今）、
IEEE自适应动态规划与增强学习技术委员会委员（2012年至今）
中国自动化学会技术过程故障诊断与安全性专业委员会委员（2010年至今）
学习体会：
大道无形，有迹可循，生活中来，生活中去。天道酬勤，厚德载物，知行合一，自强不息！

1990/09—1994 /07，包头钢铁学院（即内蒙古科技大学），机电系，工业企业电气自动化专业，本科；
1994/07—1998/08，抚顺钢厂锻压分厂，技术员；
1998/09 —2001/07，抚顺石油学院（即辽宁石油化工大学），自动化系，控制理论与控制工程专业，硕士；
2001/07—2007/03，沈阳工业学院 (即沈阳理工大学)，信息科学与工程学院，讲师；
2002/09—2006/09，东北大学，信息科学与工程学院，控制理论与控制工程专业，博士；
2007/05—2010/05，东北大学，信息科学与工程学院，博士后；
2007/03—2009/12 东北大学，信息科学与工程学院，副教授，硕士生导师，博士生副导师；
2010/01—2012/07东北大学，信息科学与工程学院，教授，硕士生导师，博士生副导师；
2012/07至今东北大学，信息科学与工程学院，教授，博士生导师。

讲授课程：
本科生：电力系统自动化
研究生：电力系统无功补偿与优化控制
曾经讲授过:自动控制原理；现代控制理论；计算机控制技术；自适应控制与系统辨识；最优控制理论；电子线路抗干扰技术

研究领域与方向：控制系统的稳定性理论、故障诊断和安全可靠性以及优化协调控制理论。具体学术研究方向为：递归神经网络稳定性理论、智能系统理论、复杂动力网络的同步与控制、故障诊断和容错控制、非线性时滞系统控制理论、无功补偿与优化控制、电力系统安全稳定控制及其在智能电网中的应用等。
总论
在稳定性理论方面取的主要研究成果：
1) 基于分解与合成原理，提出了时滞矩阵分解方法，对含有形式如$x_j(t-\tau_{ij})$等一类时滞项或耦合项的非线性系统，建立了基于矩阵不等式的稳定性判据，为这类非线性系统的综合提供了理论基础。
2) 基于变步长采样原理，针对固定时滞区间$[\tau_m, \tau_M]$，提出了不等划分的时滞加权的内插值方法，拓展了等划分时滞区间的时滞分解方法；采用凸组合原理，将固定时滞终端转化为含有可调参数的柔性终端，进而将固定时滞区间转化为动态收缩时滞区间，由此提出了含有柔性终端的外压缩时滞区间方法，这两类方法从不同的方式（内插值，外压缩）来改变时滞区间的大小，进而实现多步累积、空间增维的目的，基于上述两种方法及相应的求导方法并构造适宜的李雅普诺夫函数，建立了基于矩阵不等式的稳定判据。
3) 通过同步比较研究，揭示了李雅普诺夫函数意义下的稳定性及非线性自治系统$\dot x=f(x)$中要求$f(0)=0$的内涵，由此指出了Lyapunov理论中所考虑的非线性系统本质上就是一个误差动力系统，是相对于系统内部某个固有平衡点的误差系统（自稳定）；同步性概念则是系统自稳定概念拓展，是相对于系统外的某个系统的动态或某个指定参考目标的稳定；自稳定是相对于调节作用，同步相当于跟踪作用。由此统一了稳定性与同步性的认识，稳定性本质就是相对静止，稳定性是竞争合作的相对静止状态！
在故障诊断和优化控制方面的主要研究成果待整理。

一、论文专著
学术著作：
(1) 张化光,季策,王占山，译著，递归人工神经网络的定性分析和综合，北京：科学出版社，2004。
(2)王占山著，连续时间时滞递归神经网络的稳定性，沈阳：东北大学出版社， 2007。
(3) 王占山著，复杂神经动力网络的稳定性和同步性，北京：科学出版社，2014。
(4) Zhanshan Wang, Zhenwei Liu, Chengde Zheng, Qualitative Analysis and Control of Complex Neural Networks with Delays,
Science Press and Springer, 2015
(5)王占山，单麒赫，季策，编著，动力系统基础及其稳定特性分析，东北大学出版社，2015.
(6)王占山，关焕新，编著,智能控制及其在电力系统中的应用，东北大学出版社，2015.

教材：
(7) 孙秋野，王占山，马大中，电力系统自动化，人民邮电出版社，2014，3
(一)、动力系统的稳定性、同步性和一致性
1.Wang Zhanshan, Zhang Huaguang, Jiang Bin, LMI-based approach for global asymptotic stability analysis of recurrent neural networks with various delays and structures, IEEE Transactions on Neural Networks, 2011, 22(7): 1032-1045.
2.Wang Zhanshan, Zhang Huaguang, Li Ping, An LMI approach to stability analysis of reaction-diffusion Cohen-Grossberg neural networks concerning Dirichlet boundary conditions and distributed delays, IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 2010, 40(6): 1596-1606.
3.Wang Zhanshan, Zhang Huaguang, Global asymptotic stability of reaction-diffusion Cohen-Grossberg neural networks with continuously distributed delays, IEEE Transactions on Neural Networks, 2010, 21(1): 39-49.
4.Wang Zhanshan, Zhang Huaguang, Yu Wen, Robust stability of Cohen-Grossberg neural networks via state transmission matrix, IEEE Transactions on Neural Networks, 2009, 20(1): 169-174.
5.Zhanshan Wang, Lei Liu, Qihe Shan, Huaguang Zhang, Stability Criteria for Recurrent Neural Networks With Time-Varying Delay Based on Secondary Delay Partitioning Method, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2014.** (to appear in 2015)
6.Huaguang Zhang; Zhanshan Wang; Derong Liu, A Comprehensive Review of Stability Analysis of Continuous-Time Recurrent Neural Networks, IEEE Transactions on Neural Networks and Learning Systems; Volume 25, Issue 7, 1229 -1262, July , 2014.
7.Zhang Huaguang, Wang Zhanshan, Liu Derong, Global asymptotic stability and robust stability of a class of Cohen-Grossberg neural networks with mixed delays, IEEE Transactions on Circuits and Systems I-Regular Papers, 2009, 56(3): 616-629.
8.Zhang Huaguang, Wang Zhanshan, Liu Derong, Global asymptotic stability of recurrent neural networks with multiple time-varying delays, IEEE Transactions On Neural Networks, 2008, 19(5): 855-873.
9.Zhang Huaguang, Wang Zhanshan, Liu Derong, Robust Stability Analysis for Interval Cohen-Grossberg Neural Networks With Unknown Time-Varying Delays, IEEE Transactions On Neural Networks, 2008, 19(11): 1942-1955.
10.Zhang Huaguang, Wang Zhanshan, Global asymptotic stability of delayed cellular neural networks, IEEE Transactions on Neural Networks, 2007, 18(3): 947-950.
11.Huaguang Zhang， Zhanshan Wang， Derong Liu, Robust Exponential Stability of Recurrent Neural Networks With Multiple Time-Varying Delays, IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 54, no. 8, pp. 730-734, 2007.
12.Zhang HuaGuang, Wang ZhanShan, New delay-dependent criterion for the stability of recurrent neural networks with time-varying delay, Science in China Series F-Information Sciences, 2009, 52(6): 942-948.
13.Huaguang Zhang, Junyi Wang, Zhanshan Wang, Hongjing Liang, Mode-Dependent Stochastic Synchronization for Markovian Coupled Neural Networks With Time-Varying Mode-Delays, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2014.** (to appear in 2015)
14.Zhang Huaguang, Liu Zhenwei, Huang Guang-Bin, Wang Zhanshan, Novel weighting delay based stability criteria for recurrent neural networks with time-varying delay, IEEE Transactions on Neural Networks, 2010, 21(1): 91-106.
15.Zheng Cheng-De, Zhang Huaguang, Wang Zhanshan, Novel exponential stability criteria of high-order neural networks with time-varying delays, IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 2011, 41(2): 486-496.
16.Zheng Cheng-De, Zhang Huaguang, Wang Zhanshan, Improved robust stability criteria for delayed cellular neural networks via the LMI approach, IEEE Transactions on Circuits and Systems II-Express Briefs, 2010, 57(1): 41-45.
17.Zheng Cheng-De, Zhang Huaguang, Wang Zhanshan, Delay-dependent globally exponential stability criteria for static neural networks: an LMI approach, IEEE Transactions on Circuits and Systems II-Express Brief, 2009, 56(7): 605-609.
18.Zheng Cheng-De, Zhang Huaguang, Wang Zhanshan. New delay-dependent global exponential stability criterion for cellular-type neural networks with time-varying delays, IEEE Transactions on Circuits and Systems II-Express Briefs, 2009, 56(3): 250-254.
19.Zheng Cheng-De, Zhang Huaguang, Wang Zhanshan, An augmented LKF approach involving derivative information of both state and delay, IEEE Transactions on Neural Networks, 2010, 21(7): 1100-1109.
20.Cheng-De Zheng, Qi-He Shan, Huaguang Zhang, Zhanshan Wang, On Stabilization of Stochastic Cohen-Grossberg Neural Networks With Mode-Dependent Mixed Time-Delays and Markovian Switching IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 5, pp. 800-811, 2013.
21.ZhanshanWang, Huaguang.Zhang, Synchronization stability in complex interconnected neural networks with nonsymmetric coupling, Neurocomputing, vol. 108, pp. 84-92, 2013,
22.Wang Zhanshan, Zhang Enlin, Zhang Huaguang, Ren Zhengyun, Global stability analysis of multitime-scale neural networks,: Neural Computing & Applications, 2013, 22(2): 211-217.
23.Wang Zhanshan, Zhang Huaguang, Yu Wen, Robust stability criteria for interval Cohen-Grossberg neural networks with time varying delay, Neurocomputing, 2009, 72(4-6): 1105-1110.
24.Wang Zhanshan, Zhang Huaguang, Yu Wen, Robust exponential stability analysis of neural networks with multiple time delays, Neurocomputing, 2007,70(13-15): 2534-2543.
25.Wang Zhanshan, Zhang Huaguang, Exponential stability analysis of neural networks with multiple time varying delays, Chinese Journal of Electronics, 2006, 15(4): 649-653.
26.Wang Zhanshan, Zhang Huaguang, LMI-based criteria for globally asymptotic stability of cellular neural networks with multiple delays, Chinese Journal of Electronics, 2007, 16(1): 111-114.
27.Wang Zhanshan, Zhao Yongbin, Lun Shuxian, Global stability of a class of high-order recurrent neural networks with multiple delays, Lecture Notes in Computer Science, v 7366 LNAI, p 268-277, 2012,
28.Wang, Zhanshan, Zhang Enlin, Yun Kuo, Zhang Huaguang, Universal analysis method for stability of recurrent neural networks with different multiple delays, Lecture Notes in Computer Science, v 6675 LNCS, PART 1, p 148-157, 2011.
29.Wang Zhanshan, Zhang Huaguang, Feng Jian, Stability Analysis of Recurrent Neural Networks with Distributed Delays Satisfying Lebesgue-Stieljies Measures, Lecture Notes in Computer Science, vol. 6063, pp. 504-511, Part I, 2010.
30.Zhang Huaguang, Wang Zhanshan, Liu Derong, Exponential stability analysis of neural networks with multiple time delays, Lecture Notes in Computer Science, v 3496, pp 142-148, 2005
31.Zhang Huaguang, Wang Zhanshan, Globally exponential stability analysis and estimation of the exponential convergence rate for neural networks with multiple time varying delays, Lecture Notes in Computer Science, v 3610, n PART I, p 61-70, 2005.
32.Hongjing Liang, Huaguang Zhang, Zhanshan Wang, Junyi Wang, Consensus robust output regulation of discrete-time linear multi-agent systems, IEEE/CAA Journal of Automatica Sinica, vol. 1, no. 2, 204-209, 2014.
33.Junyi Wang , Huaguang Zhang, Zhanshan Wang , Hongjing Liang, Stochastic synchronization for Markovian coupled neural networks with partial information on transition probabilities, Neurocomputing, Volume 149, Part B, 3 February 2015, Pages 983–992
34.Huang, Yujiao, Zhang, Huaguang; Wang, Zhanshan, Multistability of complex-valued recurrent neural networks with real-imaginary-type activation functions, Applied Mathematics and Computation, v 229, p 187-200, February 25, 2014
35.Wang, Junyi, Zhang, Huaguang; Wang, Zhanshan; Liang, Hongjing, Stochastic synchronization for Markovian coupled neural networks with partial information on transition probabilities, Neurocomputing, December 07, 2013
36.Cheng-De Zheng, Huaguang Zhang and Zhanshan Wang, New less-conservative stability results for uncertain stochastic neural networks with fewer slack variables, International Journal Of Robust And Nonlinear Control 2013; 23:731–753.
37.Zhang Huaguang, Huang Bonan; Gong Dawei, Wang, Zhanshan, New results for neutral-type delayed projection neural network to solve linear variational inequalities, Neural Computing and Applications, v 23, n 6, p 1753-1761, 2013
38.Gong Dawei, Huaguang Zhang, Zhanshan Wang, Dazhong Ma, Synchronization criteria for an array of neutral-type neural networks with hybrid coupling: a novel analysis approach, Neural Processing Letters, Volume, 35, no. 1, pp. 29-45, 2012.
39.Gong Dawei, Huaguang Zhang, Zhanshan Wang, Bonan Huang, Novel synchronization analysis for complex networks with hybrid coupling by handling multitude Kronecker product terms, Neurocomputing, Volume, vol. 82, pp. 14-20, April 1, 2012,
40.Huang Yujiao, Zhang Huaguang, Wang Zhanshan, Dynamical stability analysis of multiple equilibrium points in time-varying delayed recurrent neural networks with discontinuous activation functions, Neurocomputing, vol. 91, pp. 21-28, AUG 15 2012
41.Feng Jian, Wang Shenquan, Wang Zhanshan, Stochastic synchronization in an array of neural networks with hybrid nonlinear coupling, Neurocomputing, 2011, 74(18): 3808-3815.
42.Zheng Cheng-De, Zhang Huaguang, Wang Zhanshan, Novel delay-dependent criteria for global robust exponential stability of delayed cellular neural networks with norm-bounded uncertainties, Neurocomputing, 2009, 72(7-9): 1744-1754.
43.Liu Zhenwei, Zhang Huaguang, Wang Zhanshan, Novel stability criterions of a new fuzzy cellular neural networks with time-varying delays, Neurocomputing, 2009, 72(4-6): 1056-1064.
44.王占山，王军义，梁洪晶，复杂网络的相关研究及其进展，中国自动化学会通讯，2013， 34（1）：4-16
45.王占山,张化光，余文，张庆灵，基于LMI的时变时滞Cohen-Grossberg神经网络鲁棒稳定性，电子学报，2008, 36(11): 2220-2223.
46.王占山，张化光，时滞递归神经网络中神经抑制的作用，物理学报，2006，55(11): 5674-5680.
47.钟向楠，王占山，张化光，转移概率部分未知的不确定Markov跳变系统的鲁棒镇定。吉林大学学报（工学版），2012,42（6）： 1558-1562.
48.季策，张化光，王占山，一类具有时滞的广义Hopfield神经网络的全局稳定性，控制与决策，2004， 19(8): 935-938.
（二）、动力系统的故障诊断、容错控制与安全运行
1.Zhang Huaguang, Liu Jinhai, Ma Dazhong, Wang Zhanshan, Data-core-based fuzzy min-max neural network for pattern classification, IEEE Transactions on Neural Networks, 2011, 22(12-2): 2339-2352..
2.Wang Zhanshan, Li Tieshan, Zhang Huaguang, Fault tolerant synchronization for a class of complex interconnected neural networks with delay, International Journal of Adaptive Control and Signal Processing, 2014, 28: 859–881.
3.Zhanshan Wang, Huanxin Guan, and Chengde Zheng, Fault Diagnosis Observer Design for Discrete-Time Delayed Complex Interconnected Networks with Linear Coupling, Mathematical Problems in Engineering, Volume 2012, Article ID 860489, 22 pages.
4.Wang Zhanshan, Cai Chao,Wang Junyi, Zhang, Huaguang, Adaptive fault estimation of coupling connections for synchronization of complex interconnected networks, International Symposium on Neural Networks, 2013, 455-462.
5.Zhanshan Wang, Fufei Chu, Hongjing Liang,Huaguang Zhang, Fault accommodation for complete synchronization of complex neural networks, 2013 IEEE Symposium on Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2013 , 200 – 205
6.Zhanshan Wang and Huaguang Zhang, Design of a bilinear fault detection observer for singular bilinear systems, Journal of Control Theory and Applications, 2007, 5(1): 28-36.
7.Zhanshan Wang Huaguang Zhang, Design of bilinear observer for singular bilinear systems, Journal of Control Theory and Applications, 2006,4(4):413-417.
8.Liu Lei, Wang Zhanshan, Zhang Huaguang, Adaptive NN fault-tolerant control for discrete-time systems in triangular Forms with actuator fault, Neurocomputing, 2015, 152: 209-221.
9.Lei Liu, Zhanshan Wang, Jinhai Liu, Zhenwei Liu, Neural-Network-Based Adaptive Fault Estimation for a Class of Interconnected Nonlinear System with Triangular Forms, Lecture Notes in Computer Science, 2014, 8866: 110-120.
10.Ma Da-Zhong, Zhang Hua-Guang, Wang Zhan-Shan, Fault tolerant synchronization of chaotic systems based on T-S fuzzy model with fuzzy sampled-data controller, Chinese Physics B, 2010, 19(5): 0505
11.Ma Da-Zhong, Zhang Hua-Guang, Wang Zhan-Shan, Fault tolerant synchronization of chaotic systems based on T-S fuzzy model with fuzzy sampled-data controller, Chinese Physics B, 2010, 19(5): 0505.
12.王占山，张恩林，张化光，冯健，基于Hopfield神经网络的非线性系统故障估计方法，南京航空航天大学学报，2011，43(增刊)：18-21. 7月
13.王占山，张化光，故障估计的自适应观测器设计，东北大学学报(自然科学版)，2005，26(1): 221-224.
14.张化光，马大中，王占山，冯健.一类多时滞混沌系统的主从容错同步，物理学报，2010, 59(1): 147-156.
15.王占山，张化光，状态和输入多时变时滞非线性系统的干扰补偿控制器设计,应用科学学报, 2005, 23(2)：144-147.
16.王占山，张化光，王智良，Lipschitz非线性系统的鲁棒干扰抑制能力,东北大学学报(自然科学版)，2004， 25(5): 457-459.
17.王占山，李奇安，李平，不确定时滞线性系统的鲁棒容错控制，石油化工高等学校学报，2001，14(2): 74-78.
18.王占山，李平，任正云，李奇安，非线性系统的故障诊断技术，自动化与仪器仪表，2001, 5：8-10.
19.王占山，李平，任正云，广义线性系统的鲁棒镇定分析，抚顺石油学院学报，2000， 20(4): 73-75.
（三）、动力系统的优化控制
1.Xiangnan Zhong, Haibo He, Huaguang Zhang, and Zhanshan Wang, Optimal Control for Unknown Discrete-Time Nonlinear Markov Jump Systems Using Adaptive Dynamic Programming, IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 12, 2141-2155, 2014.
2.Zhanshan Wang, Longhu Quan, and Xiuchong Liu, Sensorless SPMSM Position Estimation Using Position Estimation Error Suppression Control and EKF in Wide Speed Range, Mathematical Problems in Engineering, vol. 2014 , ID 480640, 11 pages, 2014.
3.Lei Liu, Zhanshan Wang and Zhengwei Shen, Neural-Network-Based Adaptive Dynamic Surface Control for MIMO Systems with Unknown Hysteresis, 2014 IEEE Symposium Series on Computational Intelligence, December 9-12, Orlando, Florida, USA, 2014. 356-361.
4.Liang Hongjing, Zhang Huaguang, Wang Zhanshan, Zhang Jilie, Output regulation for heterogeneous linear multi-agent systems based on distributed internal model compensator, Applied Mathematics and Computation, vol. 242, pp. 736-747, 2014.
5.Hongjing Liang, Huaguang Zhang, Zhanshan Wang, Junyi Wang, Output regulation of state-coupled linear multi-agent systems with globally reachable topologies, Neurocomputing, Volume 123, pp. 337-343, 2014.
6.Hongjing Liang, Yingchun Wang, Zhanshan Wang, Huaguang Zhang, Distributed control for second-order leader-following multi-agent systems with heterogeneous leader, 2014 International Joint Conference on Neural Networks (IJCNN), pp. 1334-1338, July 6-11, 2014, Beijing, China.
7.Liu, Wei; Zhang, Huaguang; Wang, Zhanshan,A novel truncated approximation based algorithm for state estimation of discrete-time Markov jump linear systems, Signal Processing, vol. 91, no. 4, pp. 702-712, 2011.
8.Liu Wei, Zhang Huaguang, Wang Zhanshan, State Estimation for Discrete-Time Markov Jump Linear Systems Based on Orthogonal Projective Theorem, International Journal of Control Automation and Systems, vol. 10, no. 5, pp. 1049-1054, OCT 2012.
9.Liu Wei, Zhang Huaguang, Fu Jie, Wang Zhanshan, A novel suboptimal algorithm for state estimation of Markov jump linear systems, Journal of Control Theory and Applications, 2011, 9(2): 148-154 .
10.Zhanshan Wang, Zhengwei Shen, Chao Cai Kaili Jia, Adaptive Control of Wind Turbine Generator System Based on RBF-PID Neural Network, 2014 International Joint Conference on Neural Networks (IJCNN) July 6-11, 2014, Beijing, China, 538-543.
二、奖项成果
1、面向节能的复杂配电网监测控制与故障诊断关键技术研发及应用，2010年国家科技进步奖二等奖，排名第四
2、非线性系统的鲁棒自适应控制理论及应用，2010年教育部自然科学奖一等奖，
排名第五，
3、复杂非线性系统的动态分析和智能控制理论及应用，2008年辽宁省自然科学奖一等奖，排序第三。
4、分布交互式的复杂配电自动化网络智能分析技术和监测控制系统，2007年中国电子学会电子信息科学技术奖一等奖，排序第四，

三、专利
1、中国科技发明专利，一种智能微网的源-网-荷自动控制系统及控制方法，?
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2、中国科技发明专利，一种有谐波抑制功能的分散式发电无功补偿装置及方法
3、德国科技发明专利，A distributed hybrid powered smart grid system and control method thereof”, European Patent Office, Germany,
4、中国科技发明专利，一种用于谐波抑制和无功补偿的装置及方法，
5、中国科技发明专利，小型风光互补抽水蓄能并网发电系统及充放电控制方法
6、?中国科技发明专利，一种暂态电能质量检测装置和方法，
7、?中国科技发明专利，一种轻型高压直流输电系统交替潮流计算方法及装置

1、招收博士研究生专业：
控制理论与控制工程，电力电子与电力传动
2、招收全日制硕士研究生专业：
电力系统及其自动化，电力电子与电力传动，电气工程，电工理论与新技术
3、研究生培养：
自2007年至2015年4月，独立指导毕业硕士11人、协助指导毕业博士8人、协助指导毕业硕士2人。
一名硕士研究生的学位论文获得2014年度东北大学优秀硕士学位论文。

获得的荣誉
1.2007年获得东北大学优秀博士学位论文；
2.2008年获得辽宁省优秀博士学位论文；
3.2009年获得全国优秀博士学位论文提名奖；
4.2009年入选辽宁省“百千万人才工程”千人层次；
5.2010年获得辽宁省优秀博士后称号；
6.2010年入选教育部新世纪优秀人才支持计划；
7.研究生成长历程被写入全国发行的《中国研究生》杂志，并作为2010年第10期封面人物进行报道；
8.2013年入选辽宁省“百千万人才工程”百人层次人选；
社会兼职

1.IEEE Transactions on Neural Networks and Learning Systems编委(2014年1月-2014年12月)
2.自动化学报编委（2014年1月至今）
3.IEEE自适应动态规划与增强学习技术委员会委员（2012年至今）
4.中国自动化学会技术过程故障诊断与安全性专业委员会委员（2010年至今）
5.The Sixth International Symposium on Neural Networks (ISNN 2009)程序委员会委员
6.The Seventh International Symposium on Neural Networks (ISNN 2010) 程序委员会委员
7.International Conference on Intelligent Control and Information Processing (ICICIP 2010)
1.程序委员会委员
8.The International Conference on Information Science and Technology (ICIST 2011) 程序委员会委员
9.The Eighth International Symposium on Neural Networks (ISNN 2011) 程序委员会委员
10.International Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2011) 程序委员会委员

11.The Tenth International Symposium on Neural Networks (ISNN 2012) 程序委员会委员，并作为客座主编之一(赵冬斌，沈轶，王占山，胡晓林)负责将部分论文在Neural
2.Computation & Applications 杂志上发表
12.International Conference on Brain Inspired Cognitive Systems (BICS2012) 程序委员会委员，并作为客座主编之一(张化光，Amir Hussain, 刘德荣，王占山)出版论文集1部
13.The Ninth International Symposium on Neural Networks (ISNN 2013) 程序委员会委员
14.International Conference on Brain Inspired Cognitive Systems (BICS2013) 程序委员会委员
15.IEEE World Congress on Computational Intelligence (WCCI2014)的程序委员会委员
16.第33届中国控制会议(CCC2014)的程序委员会委员
17.第34届中国控制会议(CCC2015的程序委员会委员
18.IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics, IEEE Transactions on Systems, Man, and Cybernetics, Part A, IEEE Transactions on Control Systems and Technology, Neural Networks, Neurocomputing, Neural Computations and Applications, 自动化学报，控制理论与应用，信息与控制等20余种国际和国内期刊的审稿人，以及国内外学术会议的审稿人。

Wang Zhanshan received the B.S. degrees in industry electric automation from Baotou Iron and Steel Institute (now Inner Mongolia University of Science & Technology), Baotou, China, in 1994, and received the M.S. degree in control theory and control engineering from Fushun Petroleum Institute (now Liaoning Shihua University), Fushun, China, in 2001. He received the Ph.D. degree in control theory and control engineering from Northeastern University, Shenyang, China,in 2006.
He is now a professor in Northeastern University. His research interests include stability analysis of recurrent neural networks, synchronization of complex networks, fault diagnosis, fault tolerant control and nonlinear control, and their applications in power systems. .