文章基本信息

标题：Prediction of Physical Properties of Crude Oil Based on Ensemble Random Weights Neural Network
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作者：Jun Lu ; Jinliang Ding ; Changxin Liu 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：18
页码：655-660
DOI：10.1016/j.ifacol.2018.09.349
语种：English
出版社：Elsevier
摘要：AbstractPrediction of physical properties of crude oil plays a key role in the petroleum refining industry, therefore, it is of great significance to establish the prediction model of physical properties of crude oil. In this paper, we propose an ensemble random weights neural network based prediction model whose inputs are nuclear magnetic resonance (NMR) spectra and outputs are carbon residual and asphaltene of crude oil. The model uses random vector functional link (RVFL) networks as the basic components and employs the regularized negative correlation learning strategy to build neural network ensemble and the online method to learn the new data. The experiment using the practical data collected from a refinery is carried out and compared with the decorrelated neural network ensembles with random weights (DNNE), least squares support vector machine (LS-SVM), partial least squares regression (PLS) and multiple linear regression (MLR). The results indicate the effectiveness of the proposed approach.
关键词：Keywordsphysical properties of crude oilprediction modelensemble random weightsneural networkonline learning