Nonlinear Gas Turbine Modelling: a Comparison of Narmax and Neural Network Approaches
نویسنده
چکیده
In this paper two nonlinear modelling approaches are employed to derive single nonlinear models for a Rolls Royce aircraft gas turbine. The first approach is based on the estimation of a NARMAX model using conventional structure selection and parameter estimation techniques, and the second approach is based on the use of feedforward Multilayer-Perceptron (MLP) neural networks. The performances of the models derived by the two approaches are demonstrated using a range of engine tests and by analysing their static and dynamic behaviours.
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