Because of overfitting and the improvement of generalization capability (GC)available in the construction of forecasting models using artificial neural network (ANN), a newmethod is proposed for model establishment by...Because of overfitting and the improvement of generalization capability (GC)available in the construction of forecasting models using artificial neural network (ANN), a newmethod is proposed for model establishment by means of making a low-dimension ANN learning matrixthrough principal component analysis (PCA). The results show that the PC A is able to construct anANN model without the need of finding an optimal structure with the appropriate number ofhidden-layer nodes, thus avoids overfitting by condensing forecasting information, reducingdimension and removing noise, and GC is greatly raised compared to the traditional ANN and stepwiseregression techniques for model establishment.展开更多
基金This work is sponsored by the Ministry of Science and Technology of China Project "2004 DIB3J122"
文摘Because of overfitting and the improvement of generalization capability (GC)available in the construction of forecasting models using artificial neural network (ANN), a newmethod is proposed for model establishment by means of making a low-dimension ANN learning matrixthrough principal component analysis (PCA). The results show that the PC A is able to construct anANN model without the need of finding an optimal structure with the appropriate number ofhidden-layer nodes, thus avoids overfitting by condensing forecasting information, reducingdimension and removing noise, and GC is greatly raised compared to the traditional ANN and stepwiseregression techniques for model establishment.
