RESEARCH PROJECTS
1) A study of high-dimensional and small sample size data processing method by encoding priori constraints, National Natural Science Fund of China (No. 61271385). 2013.1-2016.12 (Leader)
2) A study of constrained learning algorithms encoding particle swarm optimization and the a priori information of problem, National Natural Science Fund of China (No. 60702056). 2008.1-2010.12 (Leader)
3) A study of double search algorithm encoding the a priori information and its applications in cancer diagnosis, Natural Science Fund of Jiangsu Province (No. BK2009197). 2009.7-2011.12 (Leader)
4) A study of constrained learning algorithms encoding the a priori information, Special Fund of Science Research for President Award of Chinese Academy of Sciences. 2007.1-2009.12 (Leader)
5) The Dedicated Grant for the Recipient of “Hundred Talents Program of Chinese Academy of Science, 2001.3-2004.3 (Participant)
6) The Genetic Selectivity and Optimization of Radial Basis Probabilistic Neural Networks, National Natural Science Found of China (No.60173050). 2001.1-2003.12 (Participant) SELECTED JOURNAL PAPERS SINCE 2006 1) Fei Han, Shanxiu Yang, Jian Guan, “An effective hybrid approach of gene selection and classification for microarray data based on clustering and particle swarm optimisation”, International Journal of Data Mining and Bioinformatics, (In press)
2) Fei Han, Wei Sun, Qing-Hua Ling, “A novel strategy for gene selection of microarray data Based on gene-to-class sensitivity information”, PLoS ONE 9(5): e97530. doi:10.1371/journal.pone.0097530,2014
3) Fei Han, Qing Liu, “A diversity-guided hybrid particle swarm optimization based on gradient search”, Neurocomputing, vol. 137, pp. 234-240, 2014
4) Fei Han, Hai-Fen Yao, Qing-Hua Ling, “An improved evolutionary extreme learning machine based on particle swarm optimization”, Neurocomputing, vol.116, pp.87-93,2013
5) Fei Han, Jiansheng Zhu, “Improved particle swarm optimization combined with backpropagation for feedforward neural networks”, International Journal of Intelligent Systems, vol. 28, no.3, pp. 271-288, 2013
6) Juanjuan Tu, Yongzhao Zhan, Fei Han,”Radial basis function neural network optimized by particle swarm optimization algorithm coupling with prior information”, Journal of Computational and Theoretical Nanoscience, vol. 10, pp.2866-2871, 2013
7) Fei Han, Tong-Yue Gu, Shi-Guang Ju, "An improved hybrid algorithm based on PSO and BP for feedforward neural networks", International Journal of Digital Content Technology and its Applications, vol. 5, no. 2, pp. 106-115, 2011
8) Na Zhu, Ying Zhang, Yumei Cai, Fei Han, “A game equilibrated rapid dynamic restoration strategy in ASON”, Photonic Network Communications,vol.20, no.1, pp.83 – 93, 2010
9) Fei Han, Qing-Hua Ling,De-Shuang Huang, “An improved approximation approach incorporating particle swarm optimization and a priori information into neural networks”, Neural Computing and Applications, vol.19 , pp 256-261 , 2010
10) Fei Han, Qing-Hua Ling, De-Shuang Huang, “ Modified constrained learning algorithms incorporating additional functional constraints into neural networks”, Information Sciences, vol 178, no.3, pp 907-919, 2008
11) Fei Han, De-Shuang Huang,“ A new constrained learning algorithm for function approximation by encoding a priori information into feedforward neural networks”, Neural Computing and Applications, vol.17, pp433-439, 2008
12) Fei Han, Qing-Hua Ling, “A new approach for function approximation incorporating adaptive particle swarm optimization and a priori information,” Applied Mathematics and Computation, vol.205,pp792-798,2008
13) Fei Han, De-Shuang Huang, Zhi Hua Zhu, Tiehua Rong， “The forecast of the postoperative survival time of patients suffered from non-small cell lung cancer based on PCA and extreme learning machine”, International Journal of Neural Systems, vol. 16, no. 1, pp.39-46, 2006
14) Fei Han, De-Shuang Huang,“ Improved extreme learning machine for function approximation by encoding a priori information ”, Neurocomputing, vol. 69, no. 16-18, pp. 2369-2373 , 2006
15) Fei Han, D.S.Huang, “Improved constrained learning algorithms by incorporating additional functional constraints into neural networks,” Applied Mathematics and Computation, vol. 174, no.1, pp.34-50, 2006
16) Fei Han, Xu-Qin Li, Michael R. Lyu, Tat-Ming Lok, “A modified learning algorithm incorporating additional functional constraints into neural networks”, International Journal of Pattern Recognition and Artificial Intelligence, vol.20, no.2, pp.129-142, 2006 SELECTED REFEREED CONFERENCE PAPERS SINCE 2009
1) Fei Han, Ya-Qi Wu, Yu Cui, “A hybrid approach for cancer classification based on particle swarm optimization and prior information”, Lecture Notes in Computer Science, 8794: 350-356, 2014
2) Dan Yang, Fei Han, “An improved ensemble of extreme learning machine based on attractive and repulsive particle swarm optimization”, Lecture Notes in Computer Science, 8588: 213-220, 2014
3) Min-Ru Zhao, Jian-Ming Zhang, Fei Han, “An improved extreme learning machine with adaptive growth of hidden nodes based on particle swarm optimization”, 2014 International Joint Conference on Neural Networks (IJCNN), July 6-11, 2014, Beijing, China, pp. 886-890
4) Jian Guan, Fei Han, Shanxiu Yang, “A new gene selection method for microarray data based on PSO and informativeness metric”, Lecture Notes in Artificial Intelligence, 7996:145–154,2013
5) Yu Cui, Fei Han, Shi-Guang Ju, “Gene selection and PSO-BP classifier encoding a prior information, Advances in Swarm Intelligence”, Lecture Notes in Computer Science, 6146: 335-342, 2010
6) Yu Cui, Fei Han, Shi-Guang Ju, “A diversity guided PSO combined with BP for feedforward neural networks”, 2010 3rd International Congress on Image and Signal Processing (CISP), 4: 1538 – 1542, 2010
7) Tong-Yue Gu, Shi-Guang Ju, Fei Han, “ An improved PSO algorithm encoding a priori information for nonlinear approximation”, Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence, Springer Berlin, Heidelberg, Lecture Notes in Artificial Intelligience, 5755: 223-231, 2009
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