信息与控制2011，Vol.40Issue(5)：704-709,6.DOI:10.3724/SP.J.1219.2011.00704
适用于小子样时间序列预测的动态回归极端学习机
Dynamic Regression Extreme Learning Machine and Its Application to Small-sample Time Series Prediction
张弦 1王宏力1
作者信息
- 1. 第二炮兵工程学院自动控制工程系,陕西西安710025
- 折叠
摘要
Abstract
To deal with the problem of small-sample modeling in equipment condition on-line monitoring, an on-line monitoring method based on dynamic regression extreme learning machine (DR-ELM) is proposed. Condition data of mechanical equipment are used to train a prediction model based on DR-ELM. In an iterative manner, the latest condition data are adopted and the oldest condition data are abandoned, to achieve the DR-ELM prediction model training on-line. Thus, the current condition of mechanical equipment can be effectively predicted by the method. Simulation on chaotic time series prediction and fan condition monitoring based on time series prediction indicate that the method has better performance in training computational cost and prediction accuracy in comparison with conventional condition monitoring methods based on extreme learning machine (ELM) and on-line sequential extreme learning machine (OS-ELM).关键词
极端学习机/在线训练/小子样/时间序列预测/状态监测Key words
extreme learning machine (ELM)/ on-line training/ small-sample/ time series prediction/ condition monitoring分类
信息技术与安全科学引用本文复制引用
张弦,王宏力..适用于小子样时间序列预测的动态回归极端学习机[J].信息与控制,2011,40(5):704-709,6.
