A neural network algorithm for fast pruning based on remarkable analysis
Neural network architecture designed for large-scale and the generalization is poor , presents a neural network algorithm for fast pruning based on significance analysis. The essence of the method is based on large-scale neural network perceptron as the research object, the constructor error curved surface model to analyze the network connection weights of disturbance on the network output error caused by the impact of hidden layer neurons carry remarkable analysis, direct remove redundant hidden layer neurons, reach pruning the neural network structure while improving its generalization ability pruning purposes. Experimental results show that the conventional algorithm, the optimal pruning neurosurgery in quick pruning network structure has a simpler and faster learning speed.
Significance analysis Geural network Pruning algorithm Generalization Robot
Li Fujin Huo Meijie Ren Hongge Zhao wenbin
College of Electrical Engineering, He bei United University, Tangshang 063000,China
国际会议
长沙
英文
184-188
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）