Aeroengine Exhaust Gas Temperature Prediction Using Process Neural Network with Time-Varying Threshold Functions

Abstract

To predict the aeroengine exhaust gas temperature (EGT) more precisely, a process neuron with time-varying threshold function is proposed in this paper, and then the time-varying threshold process neural network model comprised of the presented process neurons is used for EGT prediction. By introducing a group of appropriate orthogonal basis functions, the input functions, the weight functions and the threshold functions of the time-varying threshold process neural network can be expanded as linear combinations of the given orthogonal basis functions, thus to eliminate the integration operation, then to simplify the time aggregation operation. The corresponding learning algorithm is also presented, and the effectiveness of the time-varying threshold process neural network model is evaluated through the prediction of EGT series from practical aeroengine condition monitoring.