Team Leader： Jie Gui
Team Members：Shan-Wen Zhang, Jian-Xun Mi,lin Zhu, Ying-Ke Lei, Lei Tang

This team mainly focuses on the research on Feedforward Neural Network (FNN) models with outer-supervised (or teacher) signals including Multi-layer Perceptron Networks (MLPN), Radial Basis Function Networks (RBFN), Probabilistic Neural Networks (PNN), and Radial Basis Probabilistic Neural Networks (RBPNN), etc.., and particularly on the mathematical theory, learning algorithm, model and structure, generalization capability, prediction and approximation capability as well as their applications to various areas of engineering problems. In addition, some variants of FNNs such as Recurrent Neural Networks (RNN), Optical Neural Networks (ONN), Coulomb Energy Networks (CEN), Hidden Markov Models (HMM), Fuzzy Min-Max Neural Networks (FMMNN), Modular Neural Networks (MNN), Learning Committee Machines (LCM), and ARTMAP Neural Networks (ATMAPNN), etc., are also studied in this team. Further, some self-supervised learning neural networks such as Hopfield Neural Networks (HNN), Hamming Nets (HN), Learning Vector Quantization (LVQ), and Brain State in Box (BSB), etc., are been in pursuit in this team as well.

Team Leader： Jun-Feng Xia
Team Members：Hong-Qiang Wang, Chun-Hou Zheng, Xue-Ling Li, Shu-Lin Wang, Zhu-Hong You, Min Wu, Lei Yang, Mei-Ling Hou, Yang Zhao

This team mainly focuses on research in machine learning and data mining techniques applied to bioinformatics and systems biology. The main research directions include microarray gene expression data analysis, protein supersecondary structure prediction, protein-protein interaction prediction, protein function analysis and annotation, disease candidate gene identification,etc.

Team Leader：Rong-Xiang Hu
Team Members：Wei Jia, Yi-Hai Zhu, Ling-Feng Liu, Hai Min, Dan-Feng Zhu, Xue-Yang Xiao, Xiao-Dong Dai

This team mainly focuses on the research and development (R&D) as well as the systematic integration of some efficient and market-potential algorithms, and on their realizations based on advanced software platforms. Moreover, through ever renewing and deepening the R&D, the software versions can be promoted from laboratory one until they can be upgraded to the market. The research and development directions in this team include some key and efficient algorithms in Data Mining (DM), Pattern recognition (PR), Artificial Intelligence (AI) and Neural Networks (NN), etc., in particular, those algorithms listed in the Book named “Systematic Theory of Neural Networks for Pattern recognition”. The research on main application fields in this team includes some Time Sequence Predictions (TSP) problems such as Agriculture Products and Prices (APP), Stock Prediction (SP), and Products Sale (PS), etc., and some Great Capacity of Data Mining (GCDM) problems such as Internet Data, Gene Data, and Commercial Data, etc.. In addition, the applied softwares development of Pattern Recognition (PR) and Image Processing (IP), etc., supported by some efficient algorithms, is also included in this team. Nevertheless, more applications will be added and enriched in the future R&D.