本文選題：最小二乘支持向量機(jī) + 油頁巖　； 參考：《高等學(xué)�；瘜W(xué)學(xué)報(bào)》2016年10期

【摘要】：為了提高油頁巖含油率近紅外光譜分析建模的預(yù)測(cè)精度和穩(wěn)定性,開展了基于最小二乘支持向量機(jī)(LS-SVM)建模方法的對(duì)比研究.采用主成分-馬氏距離(PCA-MD)和基于蒙特卡洛采樣(MCS)2種方法進(jìn)行了奇異樣本的檢測(cè),采用徑向基核函數(shù)的LS-SVM、偏最小二乘(PLS)和反向傳播神經(jīng)網(wǎng)絡(luò)(BPANN)3種方法進(jìn)行建模方法對(duì)比.結(jié)果表明,對(duì)于64個(gè)油頁巖巖芯樣本,與PCA-MD方法相比,采用MCS方法剔除奇異樣本后所建PLS模型的預(yù)測(cè)精度提高了28%.對(duì)于MCS方法剔除奇異樣本后的58個(gè)樣品,采用KennardStone法劃分了44個(gè)樣品的校正集和14個(gè)樣品的預(yù)測(cè)集,采用2階導(dǎo)數(shù)和標(biāo)準(zhǔn)化預(yù)處理方法,建立了100個(gè)LS-SVM的校正模型,模型的預(yù)測(cè)決定系數(shù)R2平均值達(dá)到0.90以上,高于PLS和BPANN模型的對(duì)應(yīng)值;且R2的變化量(0.02)小于BPANN模型的對(duì)應(yīng)值(0.32).因此,MCS奇異樣本檢測(cè)結(jié)合LS-SVM方法可提高油頁巖含油率樣本建模的精度和穩(wěn)定性.
[Abstract]:In order to improve the prediction accuracy and stability of oil shale oil content near infrared spectroscopy (NIR) modeling, a comparative study on the modeling method based on least squares support vector machine (LS-SVM) was carried out. Two methods, principal component Markov distance (PCA-MD) and Monte Carlo sampling (MCSN), are used to detect singular samples. Three modeling methods, LS-SVM, partial least squares (PLS) of radial basis function (RBF) and backpropagation neural network (BPANNN), are compared. The results show that for 64 oil shale core samples, compared with PCA-MD method, the prediction accuracy of PLS model established by MCS method after eliminating singular samples is improved by 28%. For 58 samples which were excluded by MCS method, the calibration sets of 44 samples and the prediction sets of 14 samples were divided by KennardStone method. The correction model of 100 LS-SVM was established by using the second order derivative and standardized pretreatment method. The average predictive decision coefficient R2 of the model is above 0.90, which is higher than the corresponding value of PLS and BPANN model, and the variation of R2 is 0.02) less than the corresponding value of BPANN model. Therefore, the accuracy and stability of oil shale oil content sample modeling can be improved by using MCS singular sample detection and LS-SVM method.
【作者單位】： 吉林大學(xué)儀器科學(xué)與電氣工程學(xué)院;
【基金】：國家潛在油氣資源(油頁巖勘探開發(fā)利用)產(chǎn)學(xué)研用合作創(chuàng)新子課題(批準(zhǔn)號(hào):OSR-02-04) 吉林省科技發(fā)展計(jì)劃項(xiàng)目重大科技專項(xiàng)(批準(zhǔn)號(hào):20116014)資助~~
【分類號(hào)】：O657.33;TE662.3

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