适用于小子样时间序列预测的动态回归极端学习机

针对设备状态在线监测中的小子样建模问题,提出一种基于动态回归极端学习机(dynamic regression extreme learning machine,DR-ELM)的设备状态在线监测方法.该方法利用设备状态数据训练基于DR-ELM的预测模型,通过逐次增加新数据与删减旧数据的方式,对DR-ELM预测模型进行在线训练,从而实现对设备状态的准确预测.混沌时间序列预测仿真与基于时间序列预测的风机状态监测实例表明,相比于极端学习机(extreme learning machine,ELM)与在线贯序极端学习机(on-line sequential extreme learning machine,OS-ELM),该方法的计算效率与预测精度更高.

Dynamic Regression Extreme Learning Machine and Its Application to Small-sample Time Series Prediction

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 effect...