甘敏，男，博士，教授，博导，IEEE高级会员。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。
电话：15215698383
主要学术论文
[1] Guodong Fan, Zishu yao, Guang-Yong Chen, Jian-Nan Su, Min GAN (甘敏, 通讯作者). IniRetinex: Rethinking Retinex-type Low-Light Image Enhancer via Initialization Perspective. The 39th Annual AAAI Conference on Artificial Intelligence. 2024. Review
[2] Fang Yuan, Min GAN (甘敏, 通讯作者), Guang-Yong Chen, C. L. Philip Chen. A Non-Alternating Algorithm for Efficient Nonnegative Matrix Factorization. Submitted to IEEE Transactions on Pattern Recognition and Machine Intelligence.
[3] Min GAN (甘敏), Xiangxiang Su, Guang-Yong Chen, Jing Chen, C. L. Philip Chen. Online Learning Under A Separable Stochastic Approximation Framework. IEEE Transactions on Pattern Recognition and Machine Intelligence. 2024, in press. Doi: 10.1109/TPAMI.2024.3495783
[4] Jian-nan Su, Min GAN (甘敏, 通讯作者), Guang-Yong Chen, C. L. Philip Chen. Revealing the Dark Side of Non-Local Attention in Single Image Super-Resolution. IEEE Transactions on Pattern Recognition and Machine Intelligence. 2024, in press, doi: 10.1109/TPAMI.2024.3457790.
[5] Jian-nan Su, Min GAN (甘敏, 通讯作者), Guang-Yong Chen, C. L. Philip Chen. High-Similarity-Pass Attention for Single Image Super-Resolution. IEEE Transactions on Image Processing, 2024, 33: 610-624.
[6] Jian-nan Su, Min GAN (甘敏, 通讯作者), Guang-Yong Chen, C. L. Philip Chen. Global Learnable Attention for Single Image Super-Resolution. IEEE Transactions on Pattern Recognition and Machine Intelligence, 2023, 45(7): 8453-8465.
[7] Guang-Yong Chen, Min GAN (甘敏, 通讯作者), Long Chen, C. L. Philip Chen. Online identification of nonlinear systems with separable structure. IEEE Transactions on Neural Networks and Learning Systems, JUNE 2024, 35(6): 2162-2388.
[8] Guang-Yong Chen, Hui-rang Xu, Min GAN (甘敏, 通讯作者). Riemannian Acceleration for Sparse PCA with Separable Structure and Second-Order Information Exploration. Submitted to IEEE Transactions on Image Processing.
[9] Fang Yuan, Min GAN (甘敏, 通讯作者), Guang-Yong Chen. Efficient Splitting Approach for Symmetric Nonnegative Matrix Factorization. Submitted to IEEE Transactions on Neural Networks and Learning Systems.
[10] Guang-Yong Chen, Peng Xue, Min GAN (甘敏, 通讯作者), Jing Chen, Wen-Zhong Guo, C. L. Philip Chen. Variable Projection Algorithms: Theoretical Insights and A Novel Approach for Problems with Large Residual. Automatica. 2024. In press.
[11] Jin-fu Fan, Linqing Huang, Chaoyu Gong, Yang You, Min GAN (甘敏, 通讯作者), Zhongjie Wang. KMT-PLL: K-means Cross-attention Transformer for Partial Label Learning. IEEE Transactions on Neural Networks and Learning Systems, 2024, in press. 10.1109/TNNLS.2023.3347792
[12] 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. July, 2023, 68(7): 4257-4264.
[13] 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 Systems, 2022, in press. 10.1109/TNNLS.2022.3184164.
[14] Guang-Yong Chen, Hui-lang Xu, Min GAN (甘敏, 通讯作者), C. L. Philip Chen. A Variable Projection-Based Algorithm for Fault Detection and Diagnosis. IEEE Transactions on Instrumentation & Measurement, 2023, 72:1-11.
[15] Jing Chen, Min GAN (甘敏), Quanmin Zhu, Pritesh Narayan, Yanjun Liu. Robust standard gradient descent algorithm for ARX models using Aitken acceleration technique, IEEE Transactions on Cybernetics, 2022, 52(9): 9646-9655.
[16] Guo-dong Fan, Bi Fan, Min GAN (甘敏, 通讯作者), C. L. Philip Chen. Multi-scale Low-light Image Enhancement Network with Illumination Constraint. IEEE Transactions on Circuits and Systems for Video Technology, 2022, 32(11): 7403-7417.
[17] 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, 2022, 52(11): 7259-7267.
[18] 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.
[19] 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.
[20] 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.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] 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.
[26] 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.
[27] 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.
[28] 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 Systems, 2019, 30(8): 2410-2418.
[29] 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)
[30] 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.
[31] 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)
[32] 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, 2018，438：46-57.
[33] 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.
[34] 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.
[35] 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.
[36] 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.
[37] 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.
[38] 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.
[39] 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.
[40] 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.
[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. 2011，26(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. 2010，30(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， 2010，32(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.
