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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

Abstract


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.

Keywords


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

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