Study on the Overfitting of the Artificial Neural Network Forecasting Model
Because of overfitting and the improvement of generalization capability (GC) available in the construction of forecasting models using artificial neural network (ANN), a new method is proposed for model establishment by means of making a low-dimension ANN learning matrix through principal component analysis (PCA). The results show that the PCA is able to construct an ANN model without the need of finding an optimal structure with the appropriate number of hidden-layer nodes, thus avoids overfitting by condensing forecasting information, reducing dimension and removing noise, and GC is greatly raised compared to the traditional ANN and stepwise regression techniques for model establishment.
作 者： JIN Long KUANG Xueyuan HUANG Haihong QIN Zhinian WANG Yehong 作者单位： JIN Long,HUANG Haihong(Guangxi Research Institute of Meteorological Disasters Mitigation, Nanning 530022)KUANG Xueyuan,QIN Zhinian(Guangxi Center of Climate, Nanning 530022)
WANG Yehong(Nanjing University of Information Science and Technology, Nanjing 210044)
刊 名： 气象学报（英文版） SCI 英文刊名： ACTA METEOROLOGICA SINICA 年，卷(期)： 2005 19(2) 分类号： P4 关键词： artificial neural network generalization capability overfitting establishment of forecasting model