点击次数：

论文类型：期刊论文

发表时间：2006-12-01

发表刊物：Dalian Ligong Daxue Xuebao/Journal of Dalian University of Technology

收录刊物：Scopus、EI、PKU、ISTIC

卷号：46

期号：S1

页面范围：174-177

ISSN号：10008608

关键字：Computer simulation; Forecasting; Neural networks; Principal component analysis, Bayesian regulation; Generalization capability, Runoff

摘要：Aiming at the too complex structure of artificial neural network when applied for long-term prediction of runoff, which may cause the overfitting problem, the model is improved by using the techniques of principal component analysis and Bayesian regulation. First, principal component analysis is used for dimensionality reduction and optimization; then, Bayesian regulation is applied to simplify the network by limiting the weights, so the generalization capability of the neural network is enhanced. The simulation results of runoff from Jiangqiao Station at Nenjiang show that the proposed model has a remarkable improvement in the generalization capability and prediction accuracy, and the neural network can converge stably.