|Table of Contents|

Novel modeling method based on improved extreme learningmachine algorithm for gasoline octane number detectionby near infrared spectroscopy(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2017年05期
Page:
660-
Research Field:
Publishing date:

Info

Title:
Novel modeling method based on improved extreme learningmachine algorithm for gasoline octane number detectionby near infrared spectroscopy
Author(s):
Hu BixiaZhang HongguangLu JiangangYan YueLi XueyuanHan JinhouLiu TongChen JinshuiSun Youxian
State Key Laboratory of Industrial Control Technology,Zhejiang University,Hangzhou 310027,China
Keywords:
gasoline octane number near infrared spectroscopy models extreme learning machine partial least square variable importance in the projection
PACS:
O657.3
DOI:
10.14177/j.cnki.32-1397n.2017.41.05.019
Abstract:
In order to improve the accuracy of gasoline octane number detection by the near infrared(NIR)spectroscopy,an improved extreme learning machine(iELM)algorithm combined with the extreme learning machine(ELM)algorithm and the improved stacked partial least square regression based on the variable importance in the projection(VIP-SPLS)algorithm is proposed here.And it solves the problem of high dimension and high collinearity in the output matrix of hidden layer of the ELM algorithm effectively.Then the proposed method is applied to a commonly used benchmark NIR spectral data of gasoline octane number detection.The results show that,compared with the PLS model and the ELM model,the accuracy of iELM model is increased by 20.0% and 29.3% respectively.The experiment shows that the iELM algorithm can be applied to the gasoline octane number detection by the near infrared spectroscopy and its accuracy is satisfactory.

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