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甘敏,男,博士,教授,博导IEEE高级会员,山东省人工智能学会理事,山东省泰山学者青年专家。主要研究方向为:机器学习优化方法与理论计算机视觉、图像处理、系统辨识

可招收博士后、博士生、学术和专业型硕士研究生。欢迎各位同学尽早联系!

电话:15215698383

邮箱: aganmin@aliyun.com

主要学术论文

[1] Guo-dong Fan, Min GAN (甘敏, 通讯作者), C. L. Philip Chen. Multi-Scale Cross-connected Dehazing Network with Scene Depth Fusion. IEEE Transactions on Neural Networks and Learning System, Major revision, 2022.

 

[2] Guo-dong Fan, Min GAN (甘敏, 通讯作者), C. L. Philip Chen. Multi-scale Low-light Enhancement Network with Illumination Constraint. IEEE Transactions on Circuits and Systems for Video Technology, major revision, 2022.

 

[3] Guang-Yong Chen, Min GAN (甘敏, 通讯作者), Hong-Tao Zhu, C. L. Philip Chen. Online identification of nonlinear systems with separable structure. Submitted to IEEE Transactions on Neural Network and Learning Systems, 2022.

 

[4] Guang-Yong Chen, Min GAN (甘敏, 通讯作者), Jing Chen, Long Chen, C. L. Philip Chen. Benefits of variable projection for solving separable nonlinear optimization problems. Submitted to IEEE Transactions on Systems, Man and Cybernetics: Systems. 2022.

 

[5] Long Chen, Guang-Yong Chen, Min GAN (甘敏通讯作者), C. L. Philip Chen. Nuisance Parameter Estimation Algorithms for Separable Nonlinear Models. IEEE Transactions on Systems, Man and Cybernetics: Systems. 2022, in press.

 

[6] Jing Chen, Biao Huang, Min GAN. Improved EM algorithms for time-delayed systems based on the gradient descent method and Arnoldi's method. IEEE Transactions on Cybernetics.

 

[7] Guang-Yong Chen, Min GAN (甘敏, 通讯作者), Jing Chen, Long Chen. Embedded Point Iteration Based Recursive Algorithm for Online Identification of Nonlinear Regression Models. IEEE Transactions on Automatic Control, Major revision

 

[8] Jing Chen, Ma Jun-xia, Min GAN (甘敏) , Quanmin Zhu. Multi-direction gradient iterative algorithm: a unified framework for gradient iterative and least squares algorithms. IEEE Transactions on Automatic Control, 2021, in press.

 

[9] Guang-Yong Chen, Min GAN (甘敏, 通讯作者), Hong-Tao Zhu, C. L. Philip Chen. An Iterative Implementation of Variable Projection Algorithm for Separable Nonlinear Optimization Problems. IEEE Transactions on Systems, Man and Cybernetics: Systems, in press, 2022.

 

[10] Jing Chen, Biao Huang, Min GAN (甘敏), C. L. Philip Chen. A novel reduced-order algorithm for rational models based on Arnoldi process and Krylov subspace. Automatica, in press, 2020doi: 10.1016/j.automatica.2021.109663.

 

[11] Guang-Yong Chen, Min GAN (甘敏, 通讯作者), C. L. Philip Chen, Hong-Tao Zhu. Frequency Principle in Broad Learning System. IEEE Transactions on Neural Network and Learning Systems, 2021, in press. DOI (identifier) 10.1109/TNNLS.2021.3081568.

 

[12] Jing Chen, Min GAN (甘敏), Quanmin ZhuPritesh NarayanYanjun Liu. Robust standard gradient descent algorithm for ARX models using Aitken acceleration technique, IEEE Transactions on Cybernetics, in press, 2021. Digital Object Identifier: 10.1109/TCYB.2021.3063113.

 

[13] Jing Chen, Min GAN (甘敏), Quanmin Zhu, Yawen Mao. Varying infimum gradient descent algorithm for agent  server systems with uncertain communication network. IEEE Transactions on Instrumentation & Measurement, 2021, 701-11.

 

[14] Jia Chen, Min GAN (甘敏, 通讯作者), Guang-Yong Chen, C. L. Philip Chen. Constrained Variable Projection Optimization for a Stationary RBF-AR Model. IEEE Transactions on Systems, Man and Cybernetics: Systems, 2022, 52(3): 1882-1890.

 

[15] Min GAN (甘敏), Hong-Tao Zhu, Guang-Yong Chen, C. L. Philip Chen. Weighted Generalized Cross Validation Based Regularization for Broad Learning System. IEEE Transactions on Cybernetics, 2020, in press, DOI (identifier): 10.1109/TCYB.2020.3015749.

 

[16] Min GAN (甘敏), Yu Guan, Guang-Yong Chen, C. L. Philip Chen. Recursive Variable Projection Algorithm for a Class of Separable Nonlinear Models. IEEE Transactions on Neural Network and Learning Systems, 2021, 32(11): 4971-4982.

 

[17] Shu-qiang Wang, Xiang-yu Wang, Yan-yan Shen, Zhi-le Yang, Min GAN (甘敏, 通讯作者), Bai-ying Lei. Diabetic Retinopathy Diagnosis using Multi-channel Generative Adversarial Network with Semi-supervision, IEEE Transactions on Automation Science and Engineering, APRIL 2021, 18(2): 574-585.

 

[18] Guang-Yong Chen, Min GAN (甘敏, 通讯作者), C. L. Philip Chen, Han-Xiong Li. Basis Function Matrix based Flexible Coefficient Autoregressive Models: A Framework for Time Series and Nonlinear System Modeling. IEEE Transactions on Cybernetics, 2021, 51(2): 614-623.

 

[19] Guang-Yong Chen, Min GAN (甘敏, 共同第一作者), Shu-qiang Wang, C. L. Philip Chen. Insights into Algorithms of Separable Nonlinear Least Squares Problems. IEEE Transactions on Image Processing, 2021, 30: 1207-1218.

 

[20] Feng Zhou, Min GAN (甘敏, 通讯作者), C. L. Philip Chen. State-dependent ARX Model-based RPC with Variable Feedback Control Laws for Output Tracking, IEEE Transactions on Industrial Electronics, 2021, 68(5): 4228-4237.

 

[21] book chapterShu-qiang Wang, Hong-fei Wang, Albert C. Cheung, Yan-yan Shen, Min GAN (甘敏, 通讯作者). Ensemble of 3D Densely Connected Convolutional Network for Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease. Deep Learning Applications2020Springer Nature Singapore Pte Ltd.

 

[22] Dong-Qing Wang, Suo Zhang, Min GAN (甘敏), and Jian-long Qiu, "A novel EM identification method for Hammerstein systems with missing output data," IEEE Transactions on Industrial Informatics, 2020, 16(4): 2500-2508. (ESI高被引论文)

 

[23] Min GAN (甘敏), Guang-Yong Chen, Long Chen, C. L. Philip Chen. Term selection for a class of nonlinear separable models. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(2): 445 - 451.

 

[24] Guang-Yong Chen, Shu-Qiang Wang, Dong-Qing Wang, Min GAN (甘敏, 通讯作者). Regularization Methods for Separable Nonlinear Models. Nonlinear Dynamics, 2019, 98: 1287–1298.

 

[25] Min GAN (甘敏), Xiao-xian Chen, Ding Feng, Guang-Yong Chen, C. L. Philip Chen. Adaptive RBF-AR Models Based on Multi-innovation Least Squares Method. IEEE Signal Processing Letters, 2019, 26(8): 1182-1186.

 

[26] Guang-Yong Chen, Min GAN (甘敏, 通讯作者), Feng Ding, C. L. Philip Chen. Modified Gram-Schmidt Method Based Variable Projection Algorithm for Separable Nonlinear Models. IEEE Transactions on Neural Networks and Learning System, 2019, 30(8): 2410-2418.

 

[27] Guang-Yong Chen, Min GAN (甘敏, 通讯作者), C. L. Philip Chen, Han-Xiong Li. A Regularized Variable Projection Algorithm for Separable Nonlinear Least Squares Problems. IEEE Transactions on Automatic Control, 2019, 64(2): 526 – 537.  (ESI高被引论文hot topic论文)

 

[28] Guang-Yong Chen, Min GAN (甘敏, 通讯作者), C. L. Philip Chen, Long Chen. A Two-Stage Estimation Algorithm Based on Variable Projection Method for GPS Positioning. IEEE Transactions on Instrumentation & Measurement, 2018, 67 (11): 2518 - 2525.

 

[29] Min GAN (甘敏), C. L. Philip Chen, Guang-Yong Chen, Long Chen. On some separated algorithms for separable nonlinear squares problems [J]. IEEE Transactions on Cybernetics, 2018, 48(10): 2866-2874. (ESI高被引论文hot topic论文)

 

[30] Guang-Yong Chen, Min GAN (甘敏, 通讯作者). Generalized Exponential Autoregressive Models for Nonlinear Time Series: Stationarity, Estimation and Applications. Information Sciences, 201843846-57.

 

[31]  Min Gan (甘敏), Long Chen, C. Y. Zhang, Hui Ping “A Self-Organizing State Space Type Microstructure Model for Financial Asset Allocation”. IEEE Access, 2016, 4: 8035-8043.

 

[32]  Min GAN(甘敏), C. L. Philip Chen, Long Chen, Chun-yang Zhang. Exploiting the Interpretability and Forecasting Ability of the RBF-AR Model for Nonlinear Time Series [J]. International Journal of Systems Science, 2016, 47(8): 1868-1876.

 

[33] Min GAN(甘敏), Han-Xiong Li, C. L. Philip Chen, Long Chen. A Potential Method for Determining Nonlinearity in Wind Data [J], IEEE Power and Energy Technology Systems Journal, 2015, 2(2): 74-81.

 

[34] Min GAN(甘敏), C. L. Philip Chen, Han-Xiong Li, Long Chen. Gradient radial basis function based varying-coefficient Autoregressive Model for nonlinear and nonstationary time series [J]. IEEE Signal Processing Letters, 2015, 22(7): 809-812.

 

[35] Min GAN(甘敏), Han-Xiong Li, Hui Peng. A variable projection approach for efficient Estimation of RBF-ARX model [J]. IEEE Transactions on Cybernetics, 2015, 45(3): 476-485.

 

[36] Chun-yang Zhang, C. L. Philip Chen, Long Chen, Min Gan(甘敏). Fuzzy Restricted Boltzmann Machine to Enhance Deep Learning [J]. IEEE Transactions on Fuzzy Systems, 2015, 23(6): 2163-2173.

 

[37] Min GAN(甘敏), Han-xiong LI. An Efficient Variable Projection Formulation for Separable Nonlinear Least Squares Problems [J]. IEEE Transactions on Cybernetics, 2014, 44(5): 707-711.

 

[38] Chun-yang Zhang, C. L. Philip Chen, Min Gan(甘敏). Predictive Deep Boltzmann Machine for Multi-Period Wind Speed forecasting [J]. IEEE Transactions on sustainable energy, 2015, 6(4): 1416-1425.

 

[39] Min Gan(甘敏), Yu Cheng, Kai Liu, Gang-lin Zhang. Seasonal time series prediction based on a quasi-linear autoregressive model [J]. Applied Soft Computing, 2014, 24(1): 13-18.

 

[40] Geng Zhang, Han-Xiong Li, Min GAN(甘敏). Design a Wind Speed Prediction Model Using Probabilistic Fuzzy System [J], IEEE Transactions on Industrial Informatics, 2012, 8(4): 819-827.

 

[41] Min GAN(甘敏), Yun-zhi Huang, Ming Ding, Xue-ping Dong. Testing for nonlinearity in solar radiation time series by a fast method of surrogate data [J]. Solar Energy, 2012, 86(9): 2893-2896.

 

[42] Min Gan(甘敏), Hui Peng, Liyuan Chen. A Global-local Approach to Parameter Optimization of RBF-type Models [J]. Information Sciences, 2012, 197(15): 144-160.

 

[43] Min Gan(甘敏), Hui Peng, Xueping Dong. A hybrid algorithm to optimize RBF network architecture and parameters for nonlinear time series modeling [J]. Applied Mathematical Modelling, 2012, 36(7): 2911-2919.

 

[44] Min Gan(甘敏), Hui Peng. Stability analysis of RBF-network based state-dependent autoregressive model for nonlinear time series [J]. Applied Soft Computing, 2012, 12(1): 174-181.

 

[45] Min Gan(甘敏), Ming Ding, Yun-zhi Huang, Xueping Dong. The effect of different state sizes on Mycielski approach for wind speed prediction [J]. Journal of Wind Engineering & Industrial Aerodynamics, 2012, 109:89-93.  

 

[46] Min Gan(甘敏), Hui Peng, et al. A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling [J]. Information Sciences, 2010, 180: 4370~4383.

 

[47] Min Gan(甘敏), Hui Peng, et al. An Adaptive Decision Maker for Constrained Evolutionary Optimization [J]. Applied Mathematics and Computation, 2010, 215(12): 4172~4184.

 

[48] 甘敏,丁明,董学平. 基于改进的Mycielski方法的风速时间序列预测[J]. 系统工程理论与实践2013, 33(4) : 1084-1088.

[49] 甘敏,彭辉,黄云志,董学平. 自组织状态空间模型参数初始分布搜索算法[J].自动化学报2012, 38(9): 1538-1543.

[50] 甘敏,彭辉,陈晓红. 基于金融市场微结构模型和进化算法的动态资产分配[J].系统工程学报. 201126(3): 314-321.

[51] 甘敏,彭辉,陈晓红. RBF-AR模型在非线性时间序列预测中的应用[J].系统工程理论与实践. 201030(6)1055~1061. 

[52] 甘敏,彭辉,王勇. 多目标优化与适应惩罚的混合约束优化进化算法[J]. 控制与决策, 2010, 25(3): 378~382.

[53] 甘敏,彭辉.不同基函数对RBF-ARX 模型的影响研究[J].中南大学学报. 2010, 41(6): 2231~2235.

[54] 甘敏,彭辉. 基于带回归权重的RBF-AR模型的混沌时间序列预测[J]. 系统工程与电子技术 201032(4)820~824.

[55] 甘敏,彭辉. RBF神经网络参数优化的两种混合优化算法[J]. 控制与决策, 2009, 24(8): 1172~1176.

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