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《Journal of Harbin Institute of Technology》 2002-01
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Self-adaptive feature space expanding neural network

SUN Xue quan 1, WANG Xue feng 2, FENG Ying jun 2 (1. School of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001, China; 2. School of Management, Harbin Institute of Technology, Harbin 150001, China)  
Most of the pattern recognition problems are nonlinear separable. For these complicated nonlinear separable problems, the feedforward neural network usually converges slowly. In this paper, we propose a new kind of neural network model named Self adaptive Feature Space Expanding Neural Network to overcome this problem. In this model, a self adaptive feature space expanding layer is added in front of the feedforward neural network to enhance the description of the original pattern, thus the nonlinear separable problem can be transformed into linear one or less nonlinear one, and our new model converges faster than the traditional feedforward neural network. A series of experiments, especially the two spiral problem indicate that our new model converges very fast, and has good generalization.
【Fund】: 国家自然科学基金资助项目 (699740 13 )
【CateGory Index】: TP391.4
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