摘要
采用极限学习机方法建立润滑油水分的定量预测模型。该方法利用Kennard-Stone方法对样本进行划分,以减少建模的工作量和提高建模速度。采用极限学习机方法对某特种车润滑油的水分进行定量预测,并与偏最小二乘法和BP神经网络方法进行比较。结果表明,采用极限学习机方法所建模型更加稳健,预测结果更加精确,可作为该润滑油水分含量快速检测的手段。
The quantitative prediction model of the lubricating oil moisture was established by using the extreme learn- ing machine method, in which Kennard-Stone method was used to divide the sample so as to reduce the working of model- ing and improve the modeling speed.The quantitative prediction of the lubricating oil moisture for a special vehicle was car- fled out by the extreme learning machine method, and the predicted result was compared with that by the partial least square method and BP neural network method.Results show that the model based on the extreme learning machine method is more steady and the predicted result is more accuracy.The extreme learning machine method can be used as a rapid de- tection method of the lubricating oil moisture.
出处
《润滑与密封》
CAS
CSCD
北大核心
2017年第6期79-82,125,共5页
Lubrication Engineering
关键词
润滑油
红外光谱
水分
极限学习机
lubricating oil
infrared spectrum
moisture
extreme learning machine
