本文選題：AdaBoost算法 + 軟測(cè)量　； 參考：《化工學(xué)報(bào)》2017年05期

【摘要】：針對(duì)PX氧化過(guò)程中4-CBA含量無(wú)法在線測(cè)量的問(wèn)題,提出了一種基于雙閾值更新樣本權(quán)重的AdaBoost算法,該算法以BP神經(jīng)網(wǎng)絡(luò)作為弱學(xué)習(xí)器,采用輪盤(pán)賭方法根據(jù)樣本權(quán)重在訓(xùn)練樣本集中選擇部分樣本訓(xùn)練弱學(xué)習(xí)器,采用上一輪弱學(xué)習(xí)器的訓(xùn)練相對(duì)誤差絕對(duì)值來(lái)更新所有訓(xùn)練樣本的權(quán)重,在此基礎(chǔ)上,用雙閾值對(duì)樣本誤差范圍進(jìn)行劃分,然后用不同的權(quán)重因子與原來(lái)的樣本權(quán)值相乘實(shí)現(xiàn)樣本權(quán)值的二次更新。該過(guò)程降低了含有大誤差的樣本的權(quán)值,增加了較大誤差的樣本的權(quán)值,從而減小了在下一輪訓(xùn)練過(guò)程中選到異常樣本的概率。分別采用5種不同的方法并用實(shí)測(cè)的工業(yè)數(shù)據(jù)建立了4-CBA含量軟測(cè)量模型,仿真結(jié)果表明用提出的改進(jìn)AdaBoost算法建立的4-CBA含量軟測(cè)量模型,其預(yù)測(cè)誤差小于其他方法建立的模型誤差。
[Abstract]:In order to solve the problem that 4-CBA content can not be measured on line during PX oxidation, a new AdaBoost algorithm based on double threshold to update the weight of samples is proposed. The BP neural network is used as a weak learner in this algorithm. The roulette method is used to select part of the training samples to train the weak learner according to the weight of the samples, and the absolute value of the relative error of the last round of the weak learner is used to update the weight of all the training samples. The range of sample error is divided by double threshold, and then the quadratic updating of sample weight is realized by multiplying different weight factors with the original sample weight. This process reduces the weight of samples with large errors and increases the weights of samples with large errors, thus reducing the probability of selecting abnormal samples in the next round of training. The soft sensing model of 4-CBA content is established by using five different methods and the measured industrial data. The simulation results show that the proposed improved AdaBoost algorithm is used to establish the soft sensor model of 4-CBA content. The prediction error is smaller than the model error established by other methods.
【作者單位】： 南京郵電大學(xué)自動(dòng)化學(xué)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61203213)~~
【分類(lèi)號(hào)】：TP183;TQ245.12
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本文編號(hào)：1895463