南京航空航天大学学报（英文版）2021，Vol.38Issue(4)：545-559,15.
基于改进正则化极限学习机的航空发动机性能参数预测
Aeroengine Performance Parameter Prediction Based on Improved Regularization Extreme Learning Machine
曹愈远 1张博文 1王华伟1
作者信息
- 1. 南京航空航天大学民航学院,南京 211106,中国
- 折叠
摘要
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.关键词
极限学习机/航空发动机/性能参数预测/前向和后向分割算法Key words
extreme learning machine/aeroengine/performance parameter prediction/forward and backward segmentation algorithms分类
航空航天引用本文复制引用
曹愈远,张博文,王华伟..基于改进正则化极限学习机的航空发动机性能参数预测[J].南京航空航天大学学报（英文版）,2021,38(4):545-559,15.
