王健英文简介
发布时间: 2018-01-24  作者:  浏览次数: 229

Address: College of Sciences,

     China University of Petroleum,

               66 Changjiang West Rd., Huangdao District,

               Qingdao Shandong, 266580, China

Email:            wangjiannl@upc.edu.cn

Telephone:      86-532-86983369 (O)

Mobile phone: 86-13061345702 (M)

  

Education

02/2017-03/2017    Visiting Proessor, Electronics and Communication Sciences Unit (ECSU) , Indian Statistical Institute, 

                               Calcutta, India.

                               Supervisor: Prof. Nikhil R. Pal

07/2013-01/2014    Postdoc, Electrical and Computer Engineering, University of Louisville, Louisville, Kentucky, USA

                               Supervisor: Prof. Jacek M. Zurada

10/2011-01/2012    Visiting Ph.D., Computational Mathematics, Xi'an Jiaotong University, China

    Supervisor: Prof. Zongben Xu

09/2010-09/2011    Joint Ph.D., Electrical and Computer Engineering, University of Louisville, Louisville, Kentucky, USA

                               Supervisor: Prof. Jacek M. Zurada

09/2006-07/2012    Ph.D., Computational Mathematics, Dalian University of Technology, Dalian, China

                               Supervisor: Prof. Wei Wu

09/1998-06/2002     B.S., Computational Mathematics, China University of Petroleum, Dongying, China

    Appointment

12/2012-present     Associate Professor, School of Sciences, China University of Petroleum, China

07/2012-12/2012     Lecturer, School of Sciences, China University of Petroleum, China

09/2002-07/2006    Lecturer, School of Sciences, China University of Petroleum, China

Research Interests

Machine Learning, Computational Mathematics, Neural Networks, Optmization

Presentations at Conferences

2017

The Fourteenth International Symposium on Neural Networks (ISNN 2017), Jun 21 - Jun 23,

Sapporo, Japan

The 24rd International Conference on Neural Information Processing (ICONIP 2017), Nov 14 - Nov 18,

Guangzhou, China.

2016

The 24th International Conference on Neural Information Processing (ICONIP), Oct. 16-21, Kyoto,

Japan

2015

The International Conference on Extreme Learning Machines (ELM2015), Dec. 15-17, Hangzhou,

China

2014

International Conference on Artificial Intelligence and Soft Computing (ICAISC), May 30-Jun. 5,

Zakopane, Poland

2012

International Symposium on Neural Networks (ISNN), Jul 18-21, Shenyang, China

2011

International Joint Conference on Neural Networks (IJCNN), Jul 31-Aug 5, San Jose, California, USA

2009

The 11th anniversary conference of Chinese Universities, Jul 20-23, Guiyang Normal University, Guiyang, China

Grants

The Study of Fault Tolerant Learning Algorithm for Feedforward Neural Networks and Its Deterministic Convergence (61305075, 250,000 RMB), supported by National Natural Science foundation of China, 01/2014-12/2016.

Convergence Analysis of Feedforward Neural Networks based on L1/2 regularization (2012M520624, 50,000RMB), supported by China Postdoctoral Science Foundation, 10/2012-10/2014.

Theoretical Analysis and Algorithm Design of Neural Networks based on L1/2 regularization method (ZR2013FQ004, 50,000RMB), supported by Natural Science Foundation of Shandong Province, 01/2013-12/2015.

Fault Tolerant Neural Networks and its Convergence Analysis (20130133120014, 50,000RMB), supported by Specialized Research Fund for the Doctoral Program of Higher Education of China, 01/2014-12/2016.

The Applications of L1/2 Regularization Neural Networks and its Theoretical Analysis (13CX02009A, 40,000RMB), supported by the Fundamental Research Funds for the Central Universities, 01/2013-12/2014.

The Model Design and Its Theoretical Analysis of Extreme Learning Machine for Deepwater Drilling Risk (15CX05053A, 100,000RMB), supported by the Fundamental Research Funds for the Central Universities, 09/2015-09/2018.

The Study and Practice of English Teaching for Information and Computing Science (150,000RMB), supported by the Projects of International Cooperation and Exchanges UPC, 01/2016-01/2019.

  

Journals Associate with:

2018.01-- IEEE Transactions on Neural Networks and Learning Systems

2014.03-- Journal of Applied Computer Science Methods

Referee papers for professional journals:

IEEE Transactions on Neural Networks and Learning Systems; Neural Networks; International Journal of Applied Mathematics and Computer Science; Neural Computing and Applications; Neurocomputing; Discrete Dynamics in Nature and Society

  

Publications and Manuscripts:

Peer-reviewed journal publications

[1]Jian Wang, Jie Yang, Wei Wu. Convergence of cyclic and almost-cyclic learning with momentum for feedforward neural networks, IEEE Transactions on Neural Networks, 22(8), pp.1297-1306, 2011. (SCI 一区)

[2]Wei Wu, Jian Wang, Mingsong Cheng, Zhengxue Li. Convergence analysis of online gradient method for BP neural networks, Neural Networks, 24(1), pp. 91-98, 2011. (SCI 二区)

[3]Jian Wang, Wei Wu, Jacek M. Zurada. Deterministic convergence of conjugate gradient method for feedforward neural networks, Neurocomputing, 74(14-15), pp. 2368-2376, 2011. (SCI 二区)

[4]Jian Wang, Wei Wu, Zhengxue Li, Long Li. Convergence of gradient method for double parallel feedforward neural network, International Journal of Numerical Analysis and Modeling, 8(3), pp. 484-495, 2011. (SCI 三区)

[5]Jian Wang, Wei Wu, Jacek M. Zurada. Boundedness and convergence of MPN for cyclic and almost cyclic learning with penalty, International Joint Conference on Neural Networks (IJCNN), Jul 31-Aug 5, 2011, pp. 125-132, San Jose, California, USA, 2011. (EI)

[6]Jian Wang, Wei Wu, Jacek M. Zurada. Computational properties and convergence analysis of BPNN for cyclic and almost cyclic learning with penalty. Neural Networks, 33, pp. 127-135, 2012. (SCI 二区)

[7]Jian Wang, Wei Wu, Jacek M. Zurada. Computational Properties of Cyclic and Almost-Cyclic learning with momentum for feedforward neural networks, International Symposium on Neural Networks (ISNN), Jul 18-21, 2012, Shenyang, Liaoning, China, 2012. (EI)

[8] 王健,中美线性代数教学方法的对比与分析,吉林工程技术师范学院学报,28(2), pp. 74-75. 2012.

Jian Wang, The Linear Algebra Teaching Methods Comparison and Analysis between China and America. Journal of Jilin Teachers Institute of Engineering and Technology, 28(2), pp. 74-75, 2012

[9]Jan Chorowski, Jian Wang, Jacek M. Zurada. Review and performance comparison of SVM- and ELM-based classifiers, Neurocomputing, 128, pp. 507-516, 2014. (SCI 二区)

[10] Wei Wu, Qinwei Fan, Jacek M. Zurada, Jian Wang, Dakun Yang, Yan Liu. Batch gradient method with smoothing L1/2 regularization for training of feedforward neural networks, Neural Networks, 50, pp. 72-78, 2014. (SCI 二区)

[11] Yan Liu, Wei Wu, Qinwei Fan, Dakun Yang, Jian Wang. A modified gradient learning algorithm with smoothing L1/2 regularization for Takagi–Sugeno fuzzy models, Neurocomputing, 138, pp. 229-237, 2014. (SCI 二区)

[12] Jian Wang, Jacek M. Zurada, Yanjiang Wang, Jing Wang, Guofang Xie. Boundedness of Weight Elimination for BP Neural Networks, The 13th International Conference on Artificial Intelligence and Soft Computing (ICAISC), June 1-5, 8467, pp. 155-165, 2014, Zakopane, Poland. (EI)

[13] Yetian Fan, Wei Wu, Wenyu Yang, Qin-wei Fan, Jian Wang. A pruning algorithm with L 1/2 regularizer for extreme learning machine. Journal of Zhejiang University - Science C 15(2): 119-125 (2014)

[14] Hongmei Shao, Jian Wang, Lijun Liu, Dongpo Xu, Wendi Bao. Relaxed conditions for convergence of batch BPAP for feedforward neural networks. Neurocomputing, 153, pp. 174-179, 2015. (SCI 二区)

[15] Bingjia Huang, Jian Wang, Yanqing Wen, Hongmei Shao, Jing Wang. Convergence analysis of inverse iterative algorithms for neural networks with L1/2 penalty. Journal of China University of Petroleum, Vol. 39, No. 2, pp. 164-170, 2015. (EI)

[16] Jian Wang, Guoling Yang, Shan Liu, Jacek M. Zurada, Convergence Analysis of Multilayer Feedforward Networks Trained with Penalty Terms, Journal of Applied Computer Science Methods, Vol. 7, No. 2, pp. 89-103, 2015.

[17] 王健, 谢国芳, 刘珊, 邵红梅, 黄炳家. 研究性教学在教学中的案例分析东南大学学报:哲学社会科学版, Vol. 17, pp.170-171, 2015.

[18] Xian Shi, Jian Wang*, Gang Liu, Liu Yang, Xinmin Ge, Shu Jiang.Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs, Journal of Natural Gas Science and Engineering, Vol. 33, pp. 687-702, 2016. (通讯作者) (SCI 三区)

[19] Xiaoling Gong, Jian Wang*, Yanjiang Wang, and Jacek M. Zurada. A Conjugate Gradient-based Efficient Algorithm for Training Single-hidden-layer Neural Networks, The 23rd International Conference on Neural Information Processing (ICONIP), Vol. 9950, pp. 470-478, 2016. (EI)

[20] Jian Wang, Zhenyun Ye, Weifeng Gao, Jacek M. Zurada. Boundedness and Convergence Analysis of Weight Elimination for Cyclic Training of Neural Networks, Neural Networks, Vol. 82, pp. 49-61, 2016. (SCI 二区)

[21] Xian Shi, Gang Liu, Yuanfang Cheng, Liu Yang, Hailong Jiang, Lei Chen, Shu Jiang, Jian Wang*. Brittleness index prediction in shale gas reservoirs based on efficient network models, Journal of Natural Gas Science and Engineering, Vol. 35, pp. 673-685, 2016. (通讯作者)(SCI 三区)

[22] Xian Shi, Gang Liu, Xiaoling Gong, Jialin Zhang, Jian Wang, and Hongning Zhang. An Efficient Approach for Real-Time Prediction of Rate of Penetration in Offshore Drilling, Mathematical Problems in Engineering, Vol. 3, pp. 1-13, 2016. (SCI 四区)

[23] .中外教师合作教学模式对师生的能力影响研究, 黑龙江教育学院学报, Vol. 35, No. 8, pp. 37-39, 2016.

[24] Jian WangQingling Cai, Qingquan Chang, Jacek M. Zuradad. Convergence Analyses on Sparse Feedforward Neural Networks via Group Lasso Regularization, Information Sciences, Vol. 381, pp. 250-269, 2017.(SCI 二区)

[25] Xian Shi, Jian Wang*, Xinmin  Ge, Zhongying  Han, Guanzheng  Qu, Shu  Jiang. A new method for rock brittleness evaluation in tight oil formation from conventional logs and petrophysical data, Journal of Petroleum Science and Engineering,151 168-182, 2017. (通讯作者) (SCI 三区)

[26] Jian Wang, Yanqing Wen, Yida Gou, Zhenyun Yeb, Hua Chen. Fractional-order gradient descent learning of BP neural networks with Caputo derivative, Neural Networks, 89: 19–30, 2017. (SCI 二区)

[27] Guoling Yang, Bingjie Zhang, Zhaoyang Sang, Jian Wang*, and Hua Chen. A Caputo-type Fractional-order gradient descent learning of BP neural networks, The 14th International Symposium on Neural Networks,  ISNN 2017. Lecture Notes in Computer Science, vol 10261, pp. 547-554, 2017 (通讯作者)(EI)

[28] Jian Wang, Guoling Yang, Bingjie Zhang, Zhanquan Sun, Yusong Liu, Jichao Wang. Convergence analysis of Caputo-type fractional order complex-valued neural networks, IEEE Access, 5: 14560-14571, 2017. (SCI 四区)

[29] Jian Wang, Yanqing Wen, Zhenyun Ye, Ling Jian and Hua Chen. Convergence analysis of BP neural networks via sparse response regularization, Applied Soft Computing, 61: 354-363, 2017. (SCI 二区)

[30] Bingjie Zhang, Tao Gao, Long Li, Zhanquan Sun, and Jian Wang*. An Improved Conjugate Gradient Neural Networks Based on a Generalized Armijo Search Method, The 24th International Conference on Neural Information Processing (ICONIP 2017), Vol. 10637, pp. 131-139, 2017.EI 通讯作者)

[31]Qin Liu, Zhaoyang Sang, Hua Chen, Jian Wang, Huaqing Zhang*. An Efficient Algorithm for Complex-Valued Neural Networks Through Training Input Weights, The 24th International Conference on Neural Information Processing (ICONIP 2017), Vol. 10637, pp. 150-159, 2017. EI

[32]Hongmin Gao, Yichen Yang, Bingyin Zhang, Long Li, Huaqing Zhang, and Shujun Wu*. Feature Selection Using Smooth Gradient L1/2 Regularization, The 24th International Conference on Neural Information Processing (ICONIP 2017), Vol. 10637, pp. 160-170, 2017.

[33] Jian Wang, Qingquan Chang, Qin Chang, Yusong Liu and Nikhil R. Pal. Weight noise injection-based MLPs with group lasso penalty: Asymptotic Convergence and application to node pruning, Submitted to IEEE Transactions on Cybernetics, 2017.

[34] Jian Wang, Bingjie Zhang, Zhanquan Sun, Wenxue Hao, Qingying Sun. A novel conjugate gradient method with generalized Armijo search for efficient training of feedforward neural networks, Neurocomputing, 275:308-3162018. doi.org/10.1016/j.neucom.2017.08.037 (SCI 二区)

[35] Jian Wang, Chen Xu, Xifeng Yang, and Jacek M. Zurada, A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks based on Group Lasso Method, In Press, IEEE Transactions on Neural Networks and learning Systems, 2018.doi.org/10.1109/TNNLS.2017.2748585 (SCI 一区)