Abstract
This paper proposes a method of forecasting the aero-engine performance parameters exhaust gas temperature (EGT) time series. In order to obtain the forecast of good quality, the similarity theory is applied to the data processing. The method, based on chaos theory and neural network, give the prediction of the EGT series with relative errors less than 1.3%, which indicate that the proposed method makes a significant contribution for accurate forecasting. These important properties may provide us with new method to make prediction and projection of the EGT time series.
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Gao, Y., Shen, Y. (2012). Prediction of Aero-Engine Exhaust Gas Temperature Based on Chaotic Neural Network Model. In: Jin, D., Lin, S. (eds) Advances in Mechanical and Electronic Engineering. Lecture Notes in Electrical Engineering, vol 176. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31507-7_25
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DOI: https://doi.org/10.1007/978-3-642-31507-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31506-0
Online ISBN: 978-3-642-31507-7
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