Application of Improved Elman Neural Network Model in Subway Settlement Monitoring
This paper proposes an improved Elman neural network prediction model based on a subway settlement monitoring data.First,it takes the advantage of local mean decomposition(LMD)in signal adaptive decomposition,and uses this algorithm to decom-pose the subway settlement monitoring sequence at multiple scales to obtain the product function(PF)with different scale characteris-tics;secondly,it takes the advantage of Elman neural network model in data series prediction to train and predict different PF compo-nents;finally,the final prediction result is obtained by reconstructing the prediction results of different PF components.The experi-ments and results show that the combination prediction model proposed in this paper has higher prediction accuracy than the single BP neural network model and Elman neural network model,in which the root mean square error(RMSE)is reduced by 1.060 2 mm and 0.069 8 mm respectively;the mean absolute error(MAE)decreased by 0.866 0 mm and 0.047 4 mm respectively;the mean abso-lute error percentage(MAPE)decreased by 0.218 9 and 0.006 8 respectively.
local mean decompositionElman neural networkcombination modelsubway settlement predictionaccuracy analysis
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