Convolution Sum Discrete Process Neural Network and Its Application in Aeroengine Exhausted Gas Temperature Prediction

The changing process of aeroengine exhausted gas temperature(EGT) is affected by complicated nonlinear time varying factors,which make it difficult to construct its mathematic model.To cope with this issue,a convolution sum discrete process neural network(CSDPNN) model is proposed and used for EGT prediction.This model directly utilizes discrete sampling points as input,and uses the convolution sum to deal with the time accumulation process.Compared with the process neural network(PNN)with continuous function inputs,there is no need to fit the sampling points to get input functions and then to decompose them by orthogonal basis functions which can lead to precision loss.Therefore,this model can achieve higher prediction precision.A learning algorithm for this model is also developed,and the model is explained and validated via Mackey-Glass chaos time series prediction.Then,the model is adopted to predict a real EGT time series.The prediction results are compared with results obtained by the process neural network with function inputs and the traditional artificial neural network(ANN),which proves that CSDPNN model has higher precision than the other two networks,and it exhibits good adaptability to EGT prediction.This model offers an effective method for real EGT prediction.