Publications

[49] Learning optimized structure of neural networks by hidden node pruning with L1 regularization

Xuetao Xie, Huaqing Zhang, Junze Wang, Qin Chang, Jian Wang*, Nikhil R. Pal

IEEE Transactions on Cybernetics (SCI 一区), 2019.


[48] Image steganalysis based on convolutional neural network and feature selection

Zhangquan Sun*, Feng Li, Huifen Huang, Jian Wang

Concurrency and Computation: Practice and Experience (SCI 四区), 2019.

[47] Construction and optimization of adaptive well pattern based on reservoir anisotropy and uncertainty

Hao Zhang, Kai Zhang*, Liming Zhang, James Sheng, Jun Yao, Jian Wang, Yongfei Yang

Journal of Petroleum Science and Engineering (SCI 二区), 2019.

[46] Unsupervised feature selection using RBF autoencoder

Ling Yu, Zhen Zhang, Xuetao Xie, Hua Chen, Jian Wang*

International Symposium on Neural Networks (EI), 2019, pp. 48-57.

[45] Conjugate gradient-based Takagi-Sugeno fuzzy neural network parameter identification and its convergence analysis

Tao Gao, Zhen Zhang, Qin Chang*, Xuetao Xie, Peng Ren, Jian Wang*

Neurocomputing(SCI 二区), vol. 364, pp. 168-181, 2019.

[44] Fractional-Order Retinex for Adaptive Contrast Enhancement of Under-Exposed Traffic Images.

Yi-Fei Pu, Ni Zhang, Zheng-Ning Wang, Jian Wang, Zhang Yi, Yan Wang, Ji-Liu Zhou

IEEE Intelligent Transportation Systems Magazine, 2019, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8637169

[43] Fully complex conjugate gradient-based neural networks using Wirtinger calculus framework: Deterministic convergence and its application.

Bingjie Zhang , Yusong Liu , Jinde Cao , Shujun Wu , Jian Wang

Neural Networks, 115: 50-64, 2019, https://doi.org/10.1016/j.neunet.2019.02.011

[42] Feature Selection for Neural Networks Using Group Lasso Regularization.

Huaqing Zhang, Jian Wang, Zhanquan Sun, Jacek M. Zurada, and Nikhil R. Pal

IEEE Transactions on Knowledge and Data Engineering, pp:1-15, 2019,https://doi.org/10.1109/TKDE.2019.2893266

[41] Weight noise injectionbased MLPs with group lasso penalty: Asymptotic Convergence and application to node pruning

Jian Wang, Qingquan Chang, Qin Chang, Yusong Liu and Nikhil R. Pal*

IEEE Transactions on Cybernetics (SCI 一区), vol. 49, no. 12, pp:4346-4364, 2019.

[40] Structure Optimization of Neural Networks with L1 Regularization on Gates.

Qin Chang, Junze Wang, Huaqing Zhang, Lina Shi, Jian Wang, Nikhil R. Pal

2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India, 2018, pp. 196-203, http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8628632&isnumber=8628618

[39] Convergence of a modified gradient-based learning algorithm with penalty for single-hidden-layer feed-forward networks

Jian Wang, Bingjie Zhang, Zhaoyang Sang, Yusong Liu, Shujun Wu, Quan Miao

Neural Computing and Applications, pp:1-12, 2018, https://link.springer.com/article/10.1007%2Fs00521-018-3748-y

[38] Fully Complex-Valued Wirtinger Conjugate Neural Networks with Generalized Armijo Search

Bingjie Zhang, Junze Wang, Shujun Wu, Jian Wang, Huaqing Zhang

International Conference on Intelligent Computing (ICIC) 2018, pp 123-133, https://link.springer.com/chapter/10.1007/978-3-319-95957-3_14

[37] An Efficient Elman Neural Networks Based on Improved Conjugate Gradient Method with Generalized Armijo Search

Mingyue Zhu, Tao Gao, Bingjie Zhang, Qingying Sun, Jian Wang

International Conference on Intelligent Computing (ICIC) 2018, pp:1-7, https://link.springer.com/chapter/10.1007/978-3-319-95930-6_1

[36] A Polak-Ribiere-Polyak Conjugate Gradient-Based Neuro-Fuzzy Network and Its Convergence.

Tao Gao, Jian Wang, Bingjie Zhang, Huaqing Zhang, Peng Ren, Nikhil R. Pal

IEEE Access, 2018.DOI: 10.1109/ACCESS.2018.2848117

[35] A New Parameter Identification Method for Type-1 TS Fuzzy Neural Network.

Tao Gao, Long Li, Zhen Zhang, Zhanquan Sun, Jian Wang

The 15th International Symposium on Neural Networks(ISNN 2018), pp. 200-207, 2018.DOI: 10.1007/978-3-319-92537-0_24

[34] A Novel Pruning Algorithm for Smoothing Feedforward Neural Networks based on Group Lasso Method

Jian Wang, Chen Xu, Xifeng Yang, Jacek M. Zurada*

IEEE Transactions on Neural Networks and learning Systems (SCI 一区), vol. 29, no. 5, pp. 2012-2024, 2018.

[33] A novel conjugate gradient method with generalized Armijo search for efficient training of feedforward neural networks.

Jian Wang, Bingjie Zhang, Zhanquan Sun, Wenxue Hao, Qingying Sun

Neurocomputing, 275:308-316,2018.doi.org/10.1016/j.neucom.2017.08.037

[32] Feature Selection Using Smooth Gradient L1/2 Regularization.

Hongmin Gao, Yichen Yang, Bingyin Zhang, Long Li, Huaqing Zhang, Shujun Wu

The 24th International Conference on Neural Information Processing (ICONIP 2017), Vol. 10637, pp. 160-170, 2017.

https://link.springer.com/chapter/10.1007%2F978-3-319-70093-9_17 (EI)

[31] An Efficient Algorithm for Complex-Valued Neural Networks Through Training Input Weights.

Qin Liu, Zhaoyang Sang, Hua Chen, Jian Wang, Huaqing Zhang

The 24th International Conference on Neural Information Processing (ICONIP 2017), Vol. 10637, pp. 150-159, 2017.

https://link.springer.com/content/pdf/10.1007%2F978-3-319-70093-9_16.pdf

[30] An Improved Conjugate Gradient Neural Networks Based on a Generalized Armijo Search Method.

Bingjie Zhang, Tao Gao, Long Li, Zhanquan Sun, Jian Wang

The 24th International Conference on Neural Information Processing (ICONIP 2017), Vol. 10637, pp. 131-139, 2017.

https://link.springer.com/chapter/10.1007/978-3-319-70093-9_14

[29] Convergence analysis of BP neural networks via sparse response regularization.

Jian Wang, Yanqing Wen, Zhenyun Ye, Ling Jian, Hua Chen

Applied Soft Computing, 61: 354-363, 2017. https://www.sciencedirect.com/science/article/pii/S1568494617304799

[28] Convergence analysis of Caputo-type fractional order complex-valued neural networks.

Jian Wang, Guoling Yang, Bingjie Zhang, Zhanquan Sun, Yusong Liu, Jichao Wang

IEEE Access, 5: 14560-14571, 2017. http://ieeexplore.ieee.org/document/7874172/

[27] A Caputo-type Fractional-order gradient descent learning of BP neural networks.

Guoling Yang, Bingjie Zhang, Zhaoyang Sang, Jian Wang,Hua Chen

The 14th International Symposium on Neural Networks, ISNN 2017. Lecture Notes in Computer Science, 10261: 547-554, 2017.

https://link.springer.com/chapter/10.1007%2F978-3-319-59072-1_64

[26] Fractional-order gradient descent learning of BP neural networks with Caputo derivative.

Jian Wang, Yanqing Wen, Yida Gou, Zhenyun Yeb, Hua Chen

Neural Networks, 89: 19–30, 2017. https://www.sciencedirect.com/science/article/pii/S0893608017300369

[25] A new method for rock brittleness evaluation in tight oil formation from conventional logs and petrophysical data.

Xian Shi, Jian Wang, Xinmin Ge, Zhongying Han, Guanzheng Qu, Shu Jiang

Journal of Petroleum Science and Engineering, 151169-182, 2017. https://www.sciencedirect.com/science/article/pii/S0920410516314164

[24] Convergence Analyses on Sparse Feedforward Neural Networks via Group Lasso Regularization.

Jian Wang, Qingling Cai, Qingquan Chang, Jacek M. Zuradad

Information Sciences, Vol. 381, pp. 250-269, 2017. https://www.sciencedirect.com/science/article/pii/S0020025516318369

[23] 中外教师合作教学模式对师生的能力影响研究

王健

黑龙江教育学院学报, Vol. 35, No. 8, pp. 37-39, 2016.

http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=hljjyxyxb201608013

[22] An Efficient Approach for Real-Time Prediction of Rate of Penetration in Offshore Drilling.

Xian Shi, Gang Liu, Xiaoling Gong, Jialin Zhang, Jian Wang, Hongning Zhang.

Mathematical Problems in Engineering, Vol. 3, pp. 1-13, 2016.

https://cn.bing.com/academic/profile?id=60d4449bc86b0bd6fbcd9d9dcab11b90&encoded=0&v=paper_preview&mkt=zh-cn (SCI 四区)

 

[21] Brittleness index prediction in shale gas reservoirs based on efficient network models.

Xian Shi, Gang Liu, Yuanfang Cheng, Liu Yang, Hailong Jiang, Lei Chen, Shu Jiang, Jian Wang

Journal of Natural Gas Science and Engineering, Vol. 35, pp. 673-685, 2016. https://www.sciencedirect.com/science/article/pii/S1875510016306497

[20] Boundedness and Convergence Analysis of Weight Elimination for Cyclic Training of Neural Networks.

Jian Wang, Zhenyun Ye, Weifeng Gao, Jacek M. Zurada

Neural Networks, Vol. 82, pp. 49-61, 2016. https://www.sciencedirect.com/science/article/pii/S0893608016300715

[19] A Conjugate Gradient-based Efficient Algorithm for Training Single-hidden-layer Neural Networks.

Xiaoling Gong, Jian Wang, Yanjiang Wang, and Jacek M. Zurada

The 23rd International Conference on Neural Information Processing (ICONIP), Vol. 9950, pp. 470-478, 2016.

https://link.springer.com/chapter/10.1007/978-3-319-46681-1_56

[18] Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs.

Xian Shi, Jian Wang, Gang Liu, Liu Yang, Xinmin Ge, Shu Jiang

Journal of Natural Gas Science and Engineering, Vol. 33, pp. 687-702, 2016.

https://www.sciencedirect.com/science/article/pii/S1875510016303742

[17] 研究性教学在教学中的案例分析

王健谢国芳刘珊邵红梅黄炳家

东南大学学报:哲学社会科学版, Vol. 17, pp.170-171, 2015.

http://xueshu.baidu.com/s?wd=paperuri%3A%28243384c62e5b4a616cd7bba6e41d7e87%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fkns.cnki.net%2FKCMS%2Fdetail%2Fdetail.aspx%3Ffilename%3Ddnds2015s2064%26dbname%3DCJFD%26dbcode%3DCJFQ&ie=utf-8&sc_us=8696310349827884645

[16] Convergence Analysis of Multilayer Feedforward Networks Trained with Penalty Terms.

Jian Wang, Guoling Yang, Shan Liu, Jacek M. Zurada

Journal of Applied Computer Science Methods, Vol. 7, No. 2, pp. 89-103, 2015.

http://xueshu.baidu.com/s?wd=paperuri%3A%281fc8a5cd1bc40f42221c53d87255af6a%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.degruyter.com%2Fview%2Fj%2Fjacsm.2015.7.issue-2%2Fjacsm-2015-0011%2Fjacsm-2015-0011.xml&ie=utf-8&sc_us=7354776417018020852 

[15] Convergence analysis of inverse iterative algorithms for neural networks with L1/2 penalty.

Bingjia Huang, Jian Wang, Yanqing Wen, Hongmei Shao, Jing Wang

Journal of China University of Petroleum, Vol. 39, No. 2, pp. 164-170, 2015.

http://xueshu.baidu.com/s?wd=paperuri%3A%2887904436c35102491f4eefcb57f1d039%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.en.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-SYDX201502027.htm&ie=utf-8&sc_us=8634014425608665934

[14] Relaxed conditions for convergence of batch BPAP for feedforward neural networks.

Hongmei Shao, Jian Wang, Lijun Liu, Dongpo Xu, Wendi Bao

Neurocomputing, 153, pp. 174-179, 2015. https://www.sciencedirect.com/science/article/pii/S0925231214016336

[13] A pruning algorithm with L 1/2 regularizer for extreme learning machine.

Yetian Fan, Wei Wu, Wenyu Yang, Qinwei Fan, Jian Wang

Journal of Zhejiang University Science C: Computer & Electronics 15(2): 119-125, 2014.

http://xueshu.baidu.com/s?wd=paperuri%3A%2811c6488c11f4d14d89c2812ed81ae9b6%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fkns.cnki.net%2FKCMS%2Fdetail%2Fdetail.aspx%3Ffilename%3Djzus201402004%26dbname%3DCJFD%26dbcode%3DCJFQ&ie=utf-8&sc_us=10975868465048509801

[12] Boundedness of Weight Elimination for BP Neural Networks.

Jian Wang, Jacek M. Zurada, Yanjiang Wang, Jing Wang, Guofang Xie

The 13th International Conference on Artificial Intelligence and Soft Computing (ICAISC), June 1-5, 8467, pp. 155-165, 2014.

https://link.springer.com/chapter/10.1007%2F978-3-319-07173-2_15

[11] A modified gradient learning algorithm with smoothing L1/2 regularization for Takagi–Sugeno fuzzy models.

Yan Liu, Wei Wu, Qinwei Fan, Dakun Yang, Jian Wang

Neurocomputing, 138, pp. 229-237, 2014. https://www.sciencedirect.com/science/article/pii/S0925231214002537

[10] Batch gradient method with smoothing L1/2 regularization for training of feedforward neural networks.

Wei Wu, Qinwei Fan, Jacek M. Zurada, Jian Wang, Dakun Yang, Yan Liu

Neural Networks, 50, pp. 72-78, 2014. https://www.sciencedirect.com/science/article/pii/S0893608013002700

[9] Review and performance comparison of SVM- and ELM-based classifiers.

Jan Chorowski, Jian Wang, Jacek M. Zurada

Neurocomputing, 128, pp. 507-516, 2014. https://www.sciencedirect.com/science/article/pii/S0925231213008825

[8] The Linear Algebra Teaching Methods Comparison and Analysis between China and America.

Jian Wang

Journal of Jilin Teachers Institute of Engineering and Technology, 28(2), pp. 74-75, 2012.

http://xueshu.baidu.com/s?wd=paperuri%3A%2826f4bddc164610a5010b7cb3c2dd7ad2%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-JLGC201202028.htm&ie=utf-8&sc_us=14801903594263500575

[7] Computational Properties of Cyclic and Almost-Cyclic learning with momentum for feedforward neural networks.

Jian Wang, Wei Wu, Jacek M. Zurada

International Symposium on Neural Networks (ISNN), Jul 18-21, 2012, Shenyang, Liaoning, China, 2012.

https://link.springer.com/chapter/10.1007/978-3-642-31346-2_61

[6] Computational properties and convergence analysis of BPNN for cyclic and almost cyclic learning with penalty.

Jian Wang, Wei Wu, Jacek M. Zurada

Neural Networks, 33, pp. 127-135, 2012. https://www.sciencedirect.com/science/article/pii/S0893608012001293

[5] Boundedness and convergence of MPN for cyclic and almost cyclic learning with penalty.

Jian Wang, Wei Wu, Jacek M. Zurada

International Joint Conference on Neural Networks (IJCNN), Jul 31-Aug 5, 2011, pp. 125-132, San Jose, California, USA, 2011.

http://xueshu.baidu.com/s?wd=paperuri%3A%28da631765bfe3928715b70d85476b5b1a%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6033210&ie=utf-8&sc_us=4200200053123753864

[4] Convergence of gradient method for double parallel feedforward neural network.

Jian Wang, Wei Wu, Zhengxue Li, Long Li

International Journal of Numerical Analysis and Modeling, 8(3), pp. 484-495, 2011.

https://cn.bing.com/academic/profile?id=775b6522b95fad0b737a0ae50662c9a2&encoded=0&v=paper_preview&mkt=zh-cn

[3] Deterministic convergence of conjugate gradient method for feedforward neural networks.

Jian Wang, Wei Wu, Jacek M. Zurada

Neurocomputing, 74(14-15), pp. 2368-2376, 2011. https://www.sciencedirect.com/science/article/pii/S0925231211002153

[2] Convergence analysis of online gradient method for BP neural networks.

Wei Wu, Jian Wang, Mingsong Cheng, Zhengxue Li

Neural Networks, 24(1), pp. 91-98, 2011. https://www.sciencedirect.com/science/article/pii/S0893608010001723

[1] Convergence of cyclic and almost-cyclic learning with momentum for feedforward neural networks.

Jian Wang, Jie Yang, Wei Wu

IEEE Transactions on Neural Networks, 22(8), pp.1297-1306, 2011.

http://xueshu.baidu.com/s?wd=paperuri%3A%2877239ab92d245fb2b745f6d74119730d%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fieeexplore.ieee.org%2Fdocument%2F5948413%2F&ie=utf-8&sc_us=13264202096500660214