福州大學數學與計算機科學學院計算機圖形學與多媒體/人工智能研究生導師介紹：甘敏

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福州大學數學與計算機科學學院計算機圖形學與多媒體/人工智能研究生導師甘敏介紹如下：

個人簡介

甘敏，男，博士，教授，博導。2010年獲中南大學控制科學與工程專業博士學位。2010年6月至2016年10月在合肥工業大學電氣與自動化工程學院工作，2016年11月至今在福州大學數學與計算機科學學院工作。2011年7月至9月在香港城市大學系統工程與工程管理學院做研究助理工作，2013年4月至12月在澳門大學科技學院做博士后工作，2015年7月至2016年5月在美國達科他州大學做博士后工作。主要研究方向為：機器學習中的優化方法、計算機視覺、系統辨識、圖像處理。主持國家自然科學基金面上項目“基于核矩陣的柔性系數回歸模型及其在風電場風速間歇性建模中的應用”，國家自然科學基金青年項目“基于變系數模型與函數逼近的非線性非平穩系統建模與預測”。

可招收計算機科學與技術專業博士生，計算機軟件與理論、應用數學、運籌學與控制論、計算機應用，軟件工程等方向的學術和專業型碩士研究生。歡迎應用數學、運籌學與控制論的學術碩士來交流。

目前我們在做計算機視覺中的超分辨率，圖像處理的去模糊，機器學習中的稀疏主成分分析、低秩矩陣分解等問題。特別歡迎數學基礎好，或對機器學習、系統建模、深度學習有一定基礎的優秀同學，盡早聯系我。期待與勤奮學習、勇于探索的同學共同進步！

電話：130557240--

郵箱： aganmin@aliyun.com

研究方向：機器學習、數據分析、圖像處理 

辦公室：數計學院2號樓510

主要學術論文

[1]  Jian-nan Su, Min GAN (甘敏, 通訊作者), Guang-Yong Chen, C. L. Philip Chen. Attention-Based Convolutional Neural Networks for Image Super-Resolution. Submitted to IEEE Transactions on Neural Networks and Learning System.

[2]  Min GAN (甘敏), Yu Guan, Guang-Yong Chen, C. L. Philip Chen. Recursive Variable Projection Algorithm for a Class of Separable Nonlinear Models. Submitted to IEEE Transactions on Signal Processing, Major revision.

[3]  Min GAN (甘敏), Hong-Tao Zhu, Guang-Yong Chen, C. L. Philip Chen. Weighted Generalized Cross Validation Based Regularization for Broad Learning System. Submitted to IEEE Transactions on Cybernetics.

[4]  Qiong-Ying Chen, Min GAN (甘敏, 通訊作者), Guang-Yong Chen, C. L. Philip Chen. Model Selection for RBF-ARX model. IEEE Transactions on Cybernetics, Major Revision.

[5]   Feng Zhou, Min GAN (甘敏, 通訊作者), C. L. Philip Chen. State-dependent ARX Model-based RPC with Variable Feedback Control Laws for Output Tracking, IEEE Transactions on Industrial Electronics, major revision.

[6]   Qiong-Ying Chen, Min GAN (甘敏, 通訊作者), C. L. Philip Chen. Variable projection approach based on BFGS algorithm for blind deconvolution, Submitted to IEEE Transactions on Computational Imaging.

[7]   Jia Chen, Min GAN (甘敏, 通訊作者), Guang-Yong Chen, C. L. Philip Chen. Constrained Variable Projection Optimization for a Stationary RBF-AR Model. Neurocomputing, Major revision.

[8]   Jing Chen, Min GAN, C. L. Philip Chen. Robust standard gradient descent algorithm for ARX models using Aitken acceleration technique, Submitted to IEEE Transactions on Automatic Control.

[9]  Guang-Yong Chen, Min GAN (甘敏, 通訊作者), Dong-Qing Wang, C. L. Philip Chen. Insights into Algorithms of Separable Nonlinear Least Squares Problems. Submitted to IEEE Transactions on Image Processing.

[10]  Yu Guan, Yun-zhi Huang, Guang-Yong Chen, Min GAN (甘敏, 通訊作者). A novel L2-norm noise constrained estimation for image restoration based on Gradient projection and variable projection, submitted to IEEE Signal Processing Letters.

[11]  Shu-qiang Wang, Xiang-yu Wang, Yan-yan Shen, Zhi-le Yang, Min GAN (甘敏, 通訊作者), Bai-ying Lei. Diabetic Retinopathy Diagnosis using Multi-channel Generative Adversarial Network with Semi-supervision, IEEE Transactions on Automation Science and Engineering, Conditionally Accept.

[12]  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, 2019, acceptable for publication, DOI: 10.1109/TII.2019.2931792.

[13]  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, acceptable for publication, 2019, DOI (identifier) 10.1109/TNNLS.2019.2904952.

[14]  Guang-Yong Chen, Min GAN (甘敏, 通訊作者), 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, acceptable for publication, DOI (identifier) 10.1109/TCYB.2019.2900469, 2019, in press.

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

[16]  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.

[17]  Guang-Yong Chen, Min GAN (甘敏, 通訊作者), 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. (ESI高被引論文，hot topic論文)

[18]  Guang-Yong Chen, Min GAN (甘敏, 通訊作者), 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.  (長文，ESI高被引論文，hot topic論文)

 [19]  Guang-Yong Chen, Min GAN (甘敏, 通訊作者), 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.

[20]  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高被引論文，hot topic論文)

[21] Guang-Yong Chen, Min GAN (甘敏, 通訊作者). Generalized Exponential Autoregressive Models for Nonlinear Time Series: Stationarity, Estimation and Applications. Information Sciences, 2018，438：46-57.

[22]  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.

[23]  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.

[24]  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.

[25]  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.

[26]  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.

[27]  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.

[28]  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.

[29]  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.

[30]  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.

[31]  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.

[32]  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.

[33]  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.

[34]  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.

[35]  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.

[36]  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. 

[37]  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.

[38]  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.

[39]  甘敏，丁明，董學平. 基于改進的Mycielski方法的風速時間序列預測[J]. 系統工程理論與實踐，2013, 33(4) : 1084-1088.

[40]  甘敏，彭輝，黃云志，董學平. 自組織狀態空間模型參數初始分布搜索算法[J].自動化學報，2012, 38(9): 1538-1543.

[41]  甘敏，彭輝，陳曉紅. 基于金融市場微結構模型和進化算法的動態資產分配[J].系統工程學報. 2011，26(3): 314-321.

[42]  甘敏，彭輝，陳曉紅. RBF-AR模型在非線性時間序列預測中的應用[J].系統工程理論與實踐. 2010，30(6)：1055~1061.

[43]  甘敏，彭輝，王勇. 多目標優化與適應懲罰的混合約束優化進化算法[J]. 控制與決策, 2010, 25(3): 378~382.

[44]  甘敏，彭輝.不同基函數對RBF-ARX 模型的影響研究[J].中南大學學報. 2010, 41(6): 2231~2235.

[45]  甘敏，彭輝. 基于帶回歸權重的RBF-AR模型的混沌時間序列預測[J]. 系統工程與電子技術， 2010，32(4)：820~824.

[46]  甘敏，彭輝. RBF神經網絡參數優化的兩種混合優化算法[J]. 控制與決策, 2009, 24(8): 1172~1176.