期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2012
卷号：5
期号：2
出版社：SERSC
摘要：When applied to wire ice-covering forecasting, the back propagation (BP) neural network is a lack of guidance for selecting the neural network initial connection weight and network structure, which contributes to the problem of a high degree of randomness and poses a difficulty for selecting an initial node with global properties. Combination traditional forecasting methods of Mean Generating Function-optimal subset regression (MGF-OSR), this paper proposes a new hybrid MGF-OSR-BP model based on Genetic Algorithm (GA) evolution BP. This paper uses the hybrid MGF-OSR-BP model based on GA evolution BP to analyze 108-ten days of ice thickness data from Erlang Mountain glacial stage, China, from 2001 to 2009.The results show that the model has a better forecast accuracy and high convergence .This paper can serve as a reference for similar middle-and long-term forecast research based on elements of time series data
