Transactions of Nanjing University of Aeronautics & Astronautics
Aeroengine Performance Parameter Prediction Based on Improved Regularization Extreme Learning Machine
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Abstract:
Performance parameter prediction technology is the core research content of aeroengine health management， and more and more machine learning algorithms have been applied in the field. Regularized extreme learning machine （RELM） is one of them. However， the regularization parameter determination of RELM consumes computational resources， which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data. This paper uses the forward and backward segmentation （FBS） algorithms to improve the RELM performance， and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm. While maintaining good generalization， the new algorithm is not sensitive to regularization parameters， which greatly saves computing resources. The experimental results on the public data sets prove the above conclusions. Finally， the new algorithm is applied to the prediction of aero-engine performance parameters， and the excellent prediction performance is achieved.
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V263.6
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