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Chaotic Radial Basis Function Neural Network Model for Prediction of Aero-Engine N1 and N2

You Gao, Yuli Shen, Keqiang Dong


The problem of model-based condition monitoring of aero-engine is considered. The similarity theory is applied to the dynamic modeling of aero-engine for obtaining the forecast of good quality. Based on chaos theory and radial basis function (RBF) neural network, this paper presents applications of the proposed technique to real-world data. Real engine data is used to investigate the performance of the proposed technique.


Engine N1 and N2, Chaotic time series, Lyapunov exponent, Radial basis function (RBF), Neural network.

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