自动化学报2018，Vol.44Issue(5)：915-921,7.DOI:10.16383/j.aas.2018.c170485
基于Regression GAN的原油总氢物性预测方法
Regression GAN Based Prediction for Physical Properties of Total Hydrogen in Crude Oil
摘要
Abstract
In view that generative adversarial network (GAN) is not applicable to prediction of physical properties of crude oil, a novel regression GAN (RGAN) framework is proposed in this study, which consists of a generator G, a discriminator D and a regression model R. Through adversarial learning between discriminator D and generator G, D extracts a series of latent features of 1H nuclear magnetic resonance spectroscopy(1H NMR)of crude oil. The first layer of latent features is shallow representation of the data space, which helps to solve the regression task. The regression model R is established using the first layer of latent features,which improves the accuracy and stability of the prediction. At the same time,the MSE loss function of the regression model R is applied to estimate the lower bound of the mutual information between conditional variables and generated samples,therefore generator G can produce more realistic samples. Experiment results demonstrate that RGAN can improve the prediction accuracy and stability of physical properties of total hydrogen in crude oil efficiently, and also improve the convergence speed of the generator as well as the quality of spectra generation.关键词
回归生成对抗网络/原油物性预测/生成对抗网络/核磁共振氢谱Key words
Regression generative adversarial network(RGAN)/prediction of crude oil properties/generative adversarial nets(GAN)/1H nuclear magnetic resonance spectroscopy(1H NMR)引用本文复制引用
郑念祖,丁进良..基于Regression GAN的原油总氢物性预测方法[J].自动化学报,2018,44(5):915-921,7.基金项目
国家自然科学基金(61590922,61525302),教育部科研业务费项目(N160801001,N161608001)资助 Supported by National Natural Science Foundation of China(61590922,61525302)and the Research Funds by the Ministry of Education of China(N160801001,N161608001) （61590922,61525302）
