This paper analyses the error sources in neural network prediction. The relationship between prediction error and quality of training sets is revealed. The influence of quality of training sets on the performance of a...This paper analyses the error sources in neural network prediction. The relationship between prediction error and quality of training sets is revealed. The influence of quality of training sets on the performance of an artificial neural network(ANN) applied in time series prediction is discussed. A numerical criterion called degree of consistency(DCT) defined from the statistical point of view for evaluating quality of training sets is introduced. Some simulation results and corresponding suggestions are presented along with the new criterion in order to properly select the training sets for neural network training.展开更多
文摘This paper analyses the error sources in neural network prediction. The relationship between prediction error and quality of training sets is revealed. The influence of quality of training sets on the performance of an artificial neural network(ANN) applied in time series prediction is discussed. A numerical criterion called degree of consistency(DCT) defined from the statistical point of view for evaluating quality of training sets is introduced. Some simulation results and corresponding suggestions are presented along with the new criterion in order to properly select the training sets for neural network training.
