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Prediction of railway coal freight turnover based on generalized regression neural network

In order to improve the prediction accuracy of railway coal cargo turnover, a prediction model based on generalized regression neural network is presented. Firstly, the key factors affecting the turnover of railway coal transport are analyzed from four aspects: natural resources, market economy and supply and demand, supply of railway transport system and competitive environment. Six key impact indicators are selected by Spearman rank correlation analysis method. The historical statistics from 2010 to 2016 are used as network learning samples, and the historical statistics from 2017 to 2019 are used as network extrapolation. A generalized regression neural network model is constructed for the sample samples, and the model solution is implemented by MATLAB program. The best smoothness factor value is selected through the network approximation error curve graph. Finally, the prediction results of the model and the actual results are analyzed for errors, and the prediction errors meet the accuracy requirements. The results of prediction are tested and analyzed, which proves the feasibility and validity of the generalized regression neural network for predicting the turnover of railway coal goods. The study and prediction of the change of freight turnover in railway coal transport is of great significance to the decision-making project input and the guarantee of freight increment.