Entropy-based Combining Prediction of Grey Time Series and Its Application
Unit crop yield prediction is an important and widely studied topic since it can have significant impact on macroeconomic regulation and agricultural structural read justment both in national wide and local.The grey system theory and neural network have individually been applied to the prediction problem of various fields,and have shown good results.However,few studies have dealt with the integration of Grey system theory and neural network for the nit crop yield prediction,though there is a great potential for useful applications in this area.In this paper,based on related concept of information entropy,which is applied to determine the weights of grey system forecasting model and RBF (radial basis function) neural network forecasting model, an entropy based combining prediction model of unit crop yield time series is proposed.This study proposes a novel combination forecasting model for improving those traditional analyzing and forecasting models as following:with respective merits of both grey forecasting model and RBF neural network forecasting model, the conjunction forecasting model is a comprehensive reflection of both social production levels and environmental factors,and it is less risky in practice,as well as relative more intuitive and feasible.
combining prediction grey system theory information entropy RBF network unit crop yield forecasting
Yue Chen Yuhong Li
Digital Engineering and Simulation Centre Huazhong University of Science and Technology Wuhan 430074 Digital Engineering and Simulation Centre Huazhong University of Science and Technology Wuhan 430074
国际会议
长沙
英文
989-992
2009-10-10（万方平台首次上网日期，不代表论文的发表时间）