From the perspective of functional analysis

the prediction of the aeroengine exhaust gas temperature could be seen as a functional approximation problem in nature.Utilizing the functional approximation capability of the process neural network

an aeroengine exhaust gas temperature prediction method was proposed based on the process neural network.In order to overcome the problem that the learning speed of the existing learning algorithm for process neural network is slow

a Levenberg-Marquardt learning algorithm based on the expansion of the orthogonal basis functions was developed.Finally

the proposed prediction method with the corresponding learning algorithm was used to predict the exhaust gas temperature of some aeroengine

and the results were satisfactory.