APU exhaust temperature prediction based on EMD-LSTM model
To improve the prediction accuracy of exhaust gas temperature(EGT),the complexity of the data should be reduced.A combined empirical modal decomposition(EMD)and long short-term memory neural network(LSTM)method was proposed to predict EGT.First,EGT data with time series characteristics were decomposed into intrinsic mode function(IMF)and residual(RES)containing the same characteristics using EMD;the components were predicted using LSTM model;and the results predicted from all components were superimposed to obtain the predicted values of EGT.The prediction results of EMD-LSTM model and single LSTM model were compared and analyzed.The results showed that the former had 35％and 42％lower root mean square error and average relative error than the latter.It indicated that this model has better prediction accuracy in predicting the EGT value of APU.
exhaust temperatureprediction accuracyempirical modal decompositionlong and short-term memory neural networkintrinsic mode function
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NSTL
万方数据
