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Probabilistic Prediction Method for Aeroengine Performance Parameters Based on Combined Optimum Relevance Vector Machine
Received date: 2012-11-19
Revised date: 2013-06-04
Online published: 2013-06-09
Supported by
Ministry Level Project
To cope with the uncertainties in the prediction process of aeroengine performance parameters, a probabilistic prediction method is proposed based on a combined optimum relevance vector machine (CORVM). Firstly, the performance parameter sequence is decomposed into sub-sequences in different frequency bands by orthogonal wavelet transform, and the prediction models of these sub-sequences based on relevance vector machine (RVM) regression are established respectively. Secondly, the quantum-behaved particle swarm optimization (QPSO) algorithm is employed to optimize the kernel parameters and embedding dimensions, which uses the minimum leave-one-out cross-validation error as the optimization target. Finally, all the prediction models are combined to obtain the final prediction values and variances. Thus the probabilistic distributions of prediction values are obtained. The validity of the proposed method is proved by experiments on aero-engine delta exhaust gas temperature prediction and lubrication metal content prediction. The experimental results show that the proposed method can avoid unreliability results and has better performance in prediction accuracy than the single prediction model.
FAN Geng , MA Dengwu . Probabilistic Prediction Method for Aeroengine Performance Parameters Based on Combined Optimum Relevance Vector Machine[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2013 , 34(9) : 2110 -2121 . DOI: 10.7527/S1000-6893.2013.0295
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