Improved RBF Neural Network Ensemble Prediction Model for PMI
Computer Science & Service System（2012）
摘要
Final prediction accuracy is greatly influenced by the predictive error of individual RBF network output in the process of RBF Neural Network Ensemble Prediction. The predictive value of individual RBF network model with the help of SAS statistical analysis software is analyzed, and the prediction values that are unreliable are removed out, Therefore the final prediction accuracy is increased. The PMI is predicted based on the improved model and former model, respectively. The results show that the relative prediction error is reduced by about 0.2%.
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关键词
employment environment indicator,radial basis function networks,prediction accuracy,rbf neural network ensemble,predictive value,economic indicators,economic composite index,pmi,statistical analysis,manufacturing data processing,manufacturing sector,production indicator,predictive error,economic health,improved rbf neural network,sas statistical analysis software,ensemble prediction model,relative prediction error,inventory levels indicator,supplier deliveries indicator,individual rbf network model,rbf neural network ensemble prediction model,individual rbf network output,rbf network,improved model,new orders indicator,prediction value,purchase management index,former model,final prediction accuracy,neural network ensemble,computational modeling,time series analysis,predictive models
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