福州大學數(shù)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(shù)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(yè)博士學位。2010年6月至2016年10月在合肥工業(yè)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(shù)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(tǒng)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(yōu)化方法、計算機視覺、系統(tǒng)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(shù)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(shù)模型與函數(shù)逼近的非線性非平穩(wěn)系統(tǒng)建模與預測”。

可招收計算機科學與技術專業(yè)博士生，計算機軟件與理論、應用數(shù)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(yè)型碩士研究生。歡迎應用數(shù)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(shù)學基礎好，或?qū)�機器學習、系統(tǒng)建模、深度學習有一定基礎的優(yōu)秀同學，盡早聯(lián)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(shù)據(jù)分析、圖像處理 

辦公室：數(shù)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(tǒng)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(tài)空間模型參數(shù)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(tài)資產(chǎn)分配[J].系統(tǒng)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(tǒng)工程理論與實踐. 2010，30(6)：1055~1061.

[43]  甘敏，彭輝，王勇. 多目標優(yōu)化與適應懲罰的混合約束優(yōu)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(shù)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(tǒng)工程與電子技術， 2010，32(4)：820~824.

[46]  甘敏，彭輝. RBF神經(jīng)網(wǎng)絡參數(shù)優(yōu)化的兩種混合優(yōu)化算法[J]. 控制與決策, 2009, 24(8): 1172~1176.