Analysis of Using a Hybrid Neural Network Forecast Model to Study Wire Ice-Covering

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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.