王建军
姓名：王建军 籍贯：宁夏惠农 民族：汉族 职称：教授（研究员） 所在部门（教研室）：beat365官方最新版 个人主页：https://wjjmath.github.io/ 办公室（电话）：beat365官方最新版305 电子邮件：wjj@swu.edu.cn; wjjmath@163.com
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2003.09 至 2006.12，西安交通大学，应用数学，博士，导师：徐宗本
2000.09 至 2003.07，宁夏大学，基础数学，硕士，导师：薛银川
1996.09 至 2000.07，宁夏大学，数学与应用数学，学士
2020.09至今, beat365手机中文官方网站 beat365官方最新版，教授，副经理
2019.04-2020.09，beat365手机中文官方网站，人工智能学院，教授，副经理
2016.03-2019.04，beat365手机中文官方网站，beat365官方最新版，教授，副经理
2012.07-2019.05，beat365手机中文官方网站，beat365官方最新版，教授
2006.12-2012.06，beat365手机中文官方网站，beat365官方最新版，副教授
2012.08-2013.08，美国Texas A&M大学，访问学者，合作导师：Ronald DeVore教授（美国科学院院士）
2008.01-2010.01，西安交通大学，力学流动站，博士后，合作导师：徐宗本教授 (中科院院士)
高维数据建模、机器学习（深度学习）、张量分析、数据挖掘、压缩感知、函数逼近论等。
逼近论，高等数学，神经网络，学习理论，数值分析，支持向量机，最优化方法，模糊数学，应用统计与数据分析，数据挖掘等
1. 2018至今任重庆市工业与应用数学学会副理事长
2. 2018至今任国际期刊《Frontiers in Applied mathematics and Statistics-Mathematics of Computation and Data Science》编委
3. 2021至今任国际期刊《Frontiers in Signal Processing》编委
4. 《EURASIP Journal on Advances in Signal Processing》特刊主编（2019）
5. 2013年至今任美国数学评论评论员
6. CSIAM全国大数据与人工智能专家委员会委员
7. 重庆市数学会常务理事，重庆市药品监督管理局药品、医疗器械、化妆品专家委员会委员
1. 耦合多重先验信息的低秩张量恢复模型、理论与算法研究. 国家自然科学基金面上项目. 执行时间：2021.01-2024.12（主持）
2. 基于样本的非线性压缩感知理论及其应用. 国家自然科学基金面上项目. 执行时间：2017.01-2020.12（主持）
3. 低秩矩阵复原的Schatten-q正则化理论与算法研究. 国家自然科学基金面上项目. 执行时间：2013.01-2016.12（主持）
4. 基于L1/2正则化的压缩传感可重构性理论研究. 国家自然科学基金青年项目. 执行时间：2011.01-2013.12（主持）
5. 关于前馈神经网络结构与本质逼近阶研究. 国家自然科学基金青年项目. 执行时间：2008.01-2011.12（主持）
6. 关于神经网络拓扑选择与逼近阶研究. 教育部科学技术重点项目. 执行时间：2008.01-2010.12（主持）
7. 关于神经网络逼近能力与算法研究. 部委级科研项目面上项目. 执行时间：2008.06-2010.06（主持）
8. 关于前向神经网络逼近复杂性与算法研究. 部委级科研项目一般项目. 执行时间：2009.06-2012.06（主持）
9. 基于Lq极小化的压缩传感理论及应用研究. 中央高校基本科研业务费重点项目，执行时间：2010.10-2013.10（主持）
10. 块稀疏信号重构的非凸极小化方法及算法应用研究. 中央高校基本科研业务费重大项目，执行时间：2015.01-2017.12（主持）
11. 网络上的流行病动力系统的研究. 国家自然科学基金青年项目. 执行时间：2008.01-2010.12（主持子课题一项）
12. 直觉模糊近似空间和形式背景中知识获取研究. 国家自然科学基金青年项目(资助金额：22万元). 执行时间：2012.01-2014.12（主研）
13. 非线性算子方程的变号解及其应用. 国家自然科学基金青年项目(资助金额：3万元). 执行时间：2009.01-2009.12（参与）
2023
§ One-bit compressed sensing via total variation minimization. Zhong Y.X., Xu C.,Zhang B., Hou J.Y., Wang J.J. Signal Processing, 2023 [pdf]
2022
§ Tensor robust principal component analysis from multi-level quantized observations. Wang J.J., Hou.J., Eldar Y.C. IEEE Transactions on Information Theory,2022 [pdf]
§ Robust low rank matrix recovery fusing local-smoothness. Liu X.L., Hou J.Y., Wang J.J. IEEE Signal Processing Letters, 2022 [pdf]
§ One-bit block sparse signal recovery via nonconvex l2/lp(0<p<1)-minimization. Chen J.Q., Gao Y., Li J.X., Wang J.J. Journal of Electronic Imaging,2022. [pdf]
§ Exact decomposition of joint low rankness and local smoothness plus sparse matrices. Peng J., Wang Y., Zhang H., Wang J.J., Meng D. IEEE Transactions on Pattern Analysis and Machine Intelligence,2022 [pdf]
§ low-rank high-order tensor completion with applications in visual data. Qin W., Wang H., Zhang F., Wang J.J. , Luo X., Huang T. IEEE Transactions on Image Processing,2022 [pdf]
§ An efficient L1-Tv solver for 1-Bit compressed sensing. Hou J., Liu X., Wang J.J. SSRN Electronic Journal,2022 [pdf]
§ Robust high-order tensor recovery via nonconvex low-rank approximation. Qin W., Wang H., Ma W., Wang J.J. Proceedings of the IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP),2022 [pdf]
2021
§ Robust low-rank tensor reconstruction using high-order t-SVD. Qin W., Wang H., Zhang F., Dai M., Wang J.J. Journal of Electronic Imaging,2021 [pdf]
§ Robust Low-tubal-rank Tensor Recovery from Binary Measurements. Hou J. , Zhang F., Qiu H., Wang J.J., Wang Y., Meng D. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021 [pdf]
§ A novel approach to large-scale dynamically weighted directed network representation. Luo X., Wu H., Wang Z., Wang J.J., Meng D. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021 [pdf]
§ Low-tubal-rank plus Sparse Tensor Recovery with Prior Subspace Information. Zhang F., Wang J.J. , Wang W.D.,Xu C. IEEE Transactions on Pattern Analysis and Machine Intelligence,2021 [pdf]
§ Low-rank matrix recovery via regularized nuclear norm minimization. Wang W., Zhang F., Wang J.J. Applied and Computational Harmonic Analysis,2021 [pdf]
§ Large-scale affine matrix rank minimization with a novel nonconvex regularizer. Wang Z., Liu Y., Luo X., Wang J.J., Gao C., Peng D., Chen W.
IEEE Transactions on Neural Networks and Learning Systems,2021 [pdf]
§ Generalized non-convex approach for low-tubal-rank tensor recovery. Wang H., Zhang F., Wang J.J., Huang T., Huang J., Liu X. IEEE Transactions on Neural Networks and Learning Systems,2021 [pdf]
§ Group sparse recovery in impulsive noise via alternating direction method of multipliers. Wang J.J., Huang J.W., Zhang F, Wang W.D. Applied and Computational Harmonic Analysis,2021 [pdf]
§ One-bit tensor completion via transformed tensor singular value decomposition. Hou J., Zhang F., Wang J.J. Applied Mathematical Modelling,2021 [pdf]
§ Estimating structural missing values via low-tubal-rank tensor completion. Wang H., Zhang F., Wang J.J., Wang Y. Proceedings of the 45th International Conference on Acoustics,2021 [pdf]
§ Low-tubal-rank tensor recovery from one-bit measurements. Hou J., Zhang F., Wang Y., Wang J.J. Proceedings of the 45th International Conference on Acoustics,2021 [pdf]
§ Non-convex sparse deviation modeling via generative models. Yang Y., Wang H., Wang J.J. IEEE International Conference on Acoustics,2021 [pdf]
§ CMCS-net: image compressed sensing with convolutional measurement via DCNN. Xie Y., Wang H., Wang J.J. IET Image Processing,2021 [pdf]
§ A denoising convolutional neural network inspired via multi-layer convolutional sparse coding. Wen Z., Wang H., Wang J.J. Journal of Electronic Imaging,2021 [pdf]
2020
§ Uniqueness guarantee of solutions of tensor tubal-rank minimization problem. Zhang F., Hou J., Wang J.J., Wang W. IEEE Signal Processing Letters,2020 [pdf]
§ One-bit Compressed sensing via lp minimization method. Hou J.Y., Wang J.J., Zhang F., Huang J.W. Inverse Problems,2020 [pdf]
§ RIP-based performance guarantee for low-tubal-rank tensor recovery. Zhang F, Wang W.D., Huang J.W., Wang J.J.,Wang Y. Journal of Computational and Applied Mathematics,2020 [pdf]
§ Tensor restricted isometry property analysis for a large class of random measurement ensembles. Zhang F, Wang W.D.,Hou J.Y., Wang J.J., Huang J.W. Science China .Information Sciences,2021 [pdf]
2019
§ A nonconvex penalty function with integral convolution approximation for compressed sensing. Wang J.J., Zhang F., Huang J.W., Wang W.D., Yuan C. Signal Processing,2019 [pdf]
§ Block-sparse signal recovery based on truncated l1- minimisation in non-Gaussian noise. Feng Q, Wang J.J.,Zhang F. IET Communications, 2019 [pdf]
§ Image denoising in impulsive noise via weighted Schatten p-norm regularization. Chen G., Wang J.J., Zhang F. Journal of Electronic Imaging,2019 [pdf]
§ Sharp sufficient condition of block signal recovery via l2/l1-minimization. Huang J.W., Wang J.J., Wang W.D. IET Signal Processing,2019 [pdf]
§ Enhanced Block-Sparse Signal Recovery Performance via Truncated ℓ2/ℓ1−2 Minimization. Kong W., Wang J.J., Wang W.D., Zhang F. Journal of Computational Mathematics,2020 [pdf]
§ Fast and efficient algorithm for matrix completion via closed-form 2/3-thresholding operator. Wang Z., Wang W., Wang J.J. Neurocomputing, 2019 [pdf]
2018
§ On asymptotic of extremes from generalized Maxwell distribution. Huang J.W., Wang J.J. Bull. Korean Math. Soc,2018 [pdf]
§ Block-sparse signal recovery via l2/l1-2minimisation method. Wang, W,D., Wang J.J., Zhang, Z.L. IET Signal Processing,2018 [pdf]
§ Reconstruction Analysis of Block Sparse Signal via Truncated ℓ2/ℓ1-minimization with Redundant Dictionaries.Jia y.L., Wang J.J.,Feng Z.
IET Signal Processing,2018 [pdf]
§ New Sufficient Conditions of Signal Recovery with Tight Frames via l1-Analysis Approach. Huang J.W., Wang J.J., Zhang F., Wang, W.D.
IEEE Access, 2018 [pdf]
§ Higher order expansion for moments of extreme for generalized Maxwell distribution. Huang J.W., Wang J.J.,Luo G.W.,Pu H. Communications in Statistics - Theory and Methods,2018 [pdf]
§ Higher order asymptotic behaviour of partial maxima of random sample from generalized Maxwell distribution under power normalization. Huang J.W., Wang J.J. Applied Mathematics-A Journal of Chinese Universities,2018 [pdf]
§ Sparse signal recovery with prior information by iterative reweighted least squares algorithm. Feng N.C., Wang J.J.,Wang W.D. Journal of Inverse and Ill-posed Problems, 2018 [pdf]
§ Perturbations of Compressed Data Separation With Redundant Tight Frames. Zhang F., Wang J.J, Wang,Y., Huang, J., &Wang W. IEEE Access,2018 [pdf]
§ An inertial projection neural network for sparse signal reconstruction via l1− 2 minimization. Zhu L., Wang J.J, He, X., & Zhao Y. Neurocomputing, 2018 [pdf]
§ Enhancing Matrix Completion Using a Modified Second-Order Total Variation. Wang W.D., Wang J.J. Discrete Dynamics in Nature and Society,2018 [pdf]
§ A Novel Thresholding Algorithm for Image Deblurring Beyond Nesterov’s Rule. Wang Z., Wang J.J., Wang W.D. IEEE Access,2018 [pdf]
2017
§ Robust Signal Recovery With Highly Coherent Measurement Matrices. Wang W.D., Wang J.J.,Zhang Z.L. IEEE Signal Processing Letters,2017 [pdf]
§ Tail properties and approximate distribution and expansion for extreme of lgmd. Huang J.W., Wang J.J, Luo G.W., He J. Journal of Inequalities & Applications,2017 [pdf]
§ On the rate of convergence of maxima for the generalized Maxwell distribution. Huang J.W., Wang J.J.,Luo G.W. Statistics: A Journal of Theoretical and Applied Statistics,2017 [pdf]
§ Non-convex block-sparse compressed sensing with redundant dictionaries. Liu C.Y., Wang J.J., Wang W.D., Wang, Z. Iet Signal Processing,2017 [pdf]
§ Improved RIP Conditions for Compressed Sensing with Coherent Tight Frames. Wang Y., Wang J.J. Discrete Dynamics in Nature and Society,2017 [pdf]
§ 基于非凸极小化的扰动压缩数据分离[J]. 刘春燕,王文东,王建军.电子学报, 2017 [pdf]
§ 基于混合l2/l1范数极小化方法的块稀疏信号重构条件[J]. 王建军,袁建军,王尧.数学学报,2017 [pdf]
§ Nonlinear Compressed Sensing Based on Kernel Sparse Representation. Nie F., Wang J.J., Wang Y., & Jing J. In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER),2017 [pdf]
2016
§ Block-sparse compressed sensing with partially known signal support via non-convex minimization. He S.Y., Wang Y,Wang J.J Xu Z.B, Iet Signal Processing,2016 [pdf]
§ 基于相干性理论的非凸块稀疏压缩感知. 王文东, 王建军, 王尧, 张自力. 中国科学 信息科学, 2016 [pdf]
§ Kernel canonical correlation analysis via gradient descent. Cai J, Tang Y,Wang J.J. Neurocomputing,2016 [pdf]
§ A perturbation analysis of block-sparse compressed sensing via mixed l2/l1 minimization. Zhang J.,Wang J.J., Wang W.D. Neurocomputing, 2016 [pdf]
§ Perona–Malik Model with a New Diffusion Coefficient for Image Denoising[J]. Yuan J., Wang J.J. International Journal of Image & Graphics,2016 [pdf]
§ Low rank tensor completion via partial sum minimization of singular values. Zhang F,Wang J.J., Jing J. International Conference on Automatic Control and Information Engineering,2016 [pdf]
2015
§ Confirming robustness of fuzzy support vector machine via ξ–α bound. Yang C Y., Wang J.J., Chou J J., et al. Neurocomputing,2015 [pdf]
§ A perturbation analysis of nonconvex block-sparse compressed sensing. Wang J.J., Zhang J., Wang W.D., et al. Communications in Nonlinear Science & Numerical Simulation,2015 [pdf]
§ 基于迭代重赋权最小二乘算法的块稀疏压缩感知. 王文东, 王尧, 王建军. 电子学报, 2015 [pdf]
2014
§ Restricted p-isometry properties of nonconvex block-sparse compressed sensin. Wang Y., Wang J.J., Xu Z.B. Signal Processing,2014 [pdf]
§ Active contours driven by local intensity and local gradient fitting energies. Yuan J.J., Wang J.J. International Journal of Pattern Recognition and Artificial Intelligence,2014 [pdf]
§ Recovery of Sparse Signal and Nonconvex Minimization. Jing J., Wang J.J.
Applied Mechanics & Materials, 2014 [pdf]
2013
§ On recovery of block-sparse signals via mixed l2/lq(0 <q<=1) minimization. Wang Y.,Wang J.J.,Xu Z.B. EURASIP Journal on Advances in Signal Processing,2013 [pdf]
§ A note on block-sparse signal recovery with coherent tight frames. Wang Y.,Wang J.J.,Xu Z.B. Discrete Dynamics in Nature and Society,2013 [pdf]
§ Lp Error estimate for minimal norm SBF interpolation. Wang J.J., Yang C. Y., Gu Z.G. Journal of Inequalities and Applications 2013 [pdf]
§ Estimation of Approximation with Jacobi Weights by Multivariate Baskakov Operator. Wang J.J., Guo H.F., Jing J. Journal of Function Spaces, 2013 [pdf]
2012
§ Derivatives of multivariate Bernstein operators and smoothness with Jacobi weights. Wang J.J., Peng Z.X.,Duan S.K., Jing J. Journal of Applied Mathematics,2012 [pdf]
§ Estimation of approximating rate for neural networks in L(w,p). Wang J.J.,Yang C.Y., Jing J. Journal of Applied Mathematics,2012 [pdf]
§ Constructive estimation of approximation for trigonometric neural networks. Wang J.J.,Xu W.H., Zou B. International Journal of Wavelets, Multiresolution and Information Processing,2012 [pdf]
§ Approximation of algebraic and trigonometric polynomials by feedforward neural networks. Wang J.J.,Chen B.L. , Yang C.Y. Neural Computing & Applications,2012 [pdf]
§ L2-Loss Twin Support Vector Machine for Classification. Gao B.B.,Wang J.J., Huang H. 5th International Conference on BioMedical Engineering and Informatics (BMEI),2012 [pdf]
§ Estimator for Fuzzy Support Vector Machine. Yang C.Y.,Wang J.J. Advanced Science Letters,2012 [pdf]
2011
§ Neural networks and the best Trigonometric approximation. Wang J.J., Xu Z.B. Journal of Systems Science and Complexity,2011 [pdf]
§ Sparse signal recovery based on lq(0 <q<=1)minimization. Wang J.J., Chen B.L., Yang C.Y. 2011 International Conference on Multimedia and Signal Processing,IEEE Computer Society,2011 [pdf]
§ Aproximation order for multivariate Durrmeyer operators with Jacobi weights. Wang J.J.,Yang C.Y., Duan S.K. Abstract & Applied Analysis,2011 [pdf]
§ Bernstein 型算子线性组合加Jacobi权逼近及高阶导数的等价定理. 彭联勇，王建军. 应用数学,2011 [pdf]
2010
§ New study of neural networks: the essential order of approximation. Wang J.J., Xu Z.B. Neural Networks,2011 [pdf]
§ Derivatives of Bernstein operators and smoothness with Jacobi weights. Wang J.J., Han G.D., et al. Taiwanese Journal of Mathematics,2011 [pdf]
§ 稳健Lq(0 <q<1)正则化理论：解的渐近分布与变量选择一致性. 常象宇,徐宗本, 张海, 王建军, 梁勇. 中国科学,2010 [pdf]
2009
§ Approximation with Jacobi weights by Baskakov operators. Taiwanese Journal of Mathematics. Wang J.J., Xu Z.B. Taiwanese Journal of Mathematics,2009. [pdf]
§ Margin calibration in SVM class-imbalanced learning. Yang C.Y., Yang J.S.,Wang J.J. Neurocomputing,2009 [pdf]
§ How to measure the essential approximation capability of a FNN. Wang J.J., Zou B., Chen B.L. 2009 Fifth International Conference on Natural Computation, IEEE Computer Society,2009 [pdf]
§ Estimation of covering number in learning theory. Wang J.J., Huang H. Luo Z.T.,Bai l.C. Fifth International Conference on Semantics, Knowledge and Grid, IEEE Computer Society,2009 [pdf]
§ Generalization performance of ERM algorithm with geometrically ergodic markov chain samples. Xu J., Zou B.,Wang J.J. Fifth International Conference on Natural Computation; IEEE Computer Society,2009 [pdf]
§ 神经网络的加权本质逼近阶. 王建军, 徐宗本. 数学年刊:中文版,2009 [pdf]
§ 多元多项式函数的三层前向神经网络逼近方法. 王建军, 徐宗本. 计算机学报:中文版,2009 [pdf]
2008
§ Baskakov算子线性组合加Jacobi权逼近及高阶导数的正逆定理. 王建军, 徐宗本. 系统科学与数学,2008 [pdf]
§ Constructive approximation method of polynomial by neural networks. Wang J.J., Xu Z.B.,Jing J. International conference on congnitive neurodynamics(2007), Springer Science Business Media B.V,2008
[pdf]
§ Imbalanced SVM learning with margin compensation. Lecture Notes in Computer Science, Germany. Yang C.Y.,Wang J.J., Yang J.S.,Yu G.D. Springer-Verlag,2008 [pdf]
2007
§ Stechkin-marchaud type inequalities with Jacobi weights for Bernstein operators. Wang J.J., Xue Y.C., Li F.J. Journal of Applied mathematics and computing,2007 [pdf]
2006
§ 近似指数型神经网络的本质逼近阶. 王建军,徐宗本. 中国科学,2006 [pdf]
§ Multiple positive radial solutions of elliptic equations in an exterior domain. Monatshefte fur mathematik. Han G.D.,Wang J.J. Monatshefte fur mathematik,2006 [pdf]
§ and Meng D.Y., Approximation bound of mixture networks in L(w,p) spaces. Lecture Notes in Computer Science, Germany. Xu Z.B., Wang J.J., and Meng D.Y. Springer-Verlag 2006 [pdf]
§ Baskakov算子加Jacobi权逼近及导数的正逆定理. 王建军,薛银川. 数学年刊,2006 [pdf]
2004
§ Baskakov型算子加权逼近下的Stechkin-Marchand不等式. 王建军,薛银川. 数学研究与评论,2004 [pdf]
§ Approximation bounds by neural networks in L(w, p). Lecture Notes in Computer Science, Germany. Wang J.J., Xu Z.B.,and Xu W.J. Springer-Verlag,2004 [pdf]
2022.09.15：基于深度卷积神经网络与压缩感知的图像恢复方法
个人获奖
1. 重庆市首批英才计划·创新创业领军人才，2019年11月
2. 第三批重庆市学术技术带头人，2019年3月
3. 《复杂结构性高维数据稀疏建模的方法与算法应用》获重庆市自然科学三等奖，2018年度
4. 全国老员工数学建模竞赛中荣获重庆赛区优秀指导教师奖，2016年
5. 全国老员工数学建模竞赛中荣获重庆赛区优秀指导教师奖，2012年
6. beat365手机中文官方网站2010-2012年学年度优秀教师
员工指导获奖
1. 2022年，第一届全国高校计算机技能竞赛（初赛），一等奖
2. 2022年，第三届全国老员工算法设计与编程挑战赛（冬季赛），金奖
3. 2021年，第十八届“华为杯”中国研究生数学建模竞赛，二等奖
4. 2021年，全国高等院校英语能力大赛，重庆市三等奖
5. 2018年，美国数学建模大赛，一等奖2项
6. 2018年，高教社杯全国老员工数学建模竞赛，重庆市一等奖，二等奖各1项
7. 2017年，指导beat365手机中文官方网站员工科技创新团队入选2017年度全国老员工“小平科技创新团队
8. 2017年，美国数学建模大赛，二等奖2项
9. 2017年，全国老员工统计建模大赛，一等奖1项，二等奖2项
10. 2017年，“国家级老员工创新创业训练计划”项目
11. 2016年，美国数学建模大赛，一等奖、二等奖各一项
12. 2016年，高教社杯全国老员工数学建模竞赛，二等奖
13. 2016年，高教社杯全国老员工数学建模竞赛，重庆市二等奖3项
14. 2015年，“国家级老员工创新创业训练计划”项目
15. 2012年，高教社杯全国老员工数学建模竞赛，一等奖
16. 2012年，高教社杯全国老员工数学建模竞赛，一等奖
17. 2010年，高教社杯全国老员工数学建模竞赛，二等奖
18. 2012年，高教社杯全国老员工数学建模竞赛，重庆市一等奖
19. 2011年，高教社杯全国老员工数学建模竞赛，重庆市一等奖
