【摘要】：隨著時間的增加,傳統(tǒng)時間差(TD)模型會出現(xiàn)性能顯著下降的問題。為了提高TD模型的可靠性和預(yù)測精度,同時考慮過程的時滯特征,基于一種選擇性集成策略,提出一種局部時間差高斯過程回歸(LTDGPR)模型的自適應(yīng)軟測量建模方法。首先,提取出數(shù)據(jù)庫中的時滯動態(tài)信息,對建模數(shù)據(jù)進行重構(gòu);然后,采取局部化策略對差分后的重構(gòu)樣本進行統(tǒng)計劃分,得到LTDGPR模型集。對于新來的輸入樣本,選擇部分泛化能力強的LTDGPR模型進行集成,估計出含一定時間差的主導(dǎo)變量動態(tài)偏移值;最后,基于TD模型思想對當(dāng)前時刻主導(dǎo)變量值進行在線預(yù)測。通過脫丁烷塔過程的數(shù)據(jù)建模仿真研究,驗證了所提方法的有效性和精度。
[Abstract]:With the increase of time, the performance of the traditional time-difference (TD) model will decrease significantly. In order to improve the reliability and prediction accuracy of TD model and take into account the time-delay characteristics of the process, an adaptive soft-sensor modeling method for Gao Si regression (LTDGPR) model with local time difference is proposed based on a selective integration strategy. First, the time-delay dynamic information is extracted from the database, and then the modeling data is reconstructed, and then the LTDGPR model set is obtained by statistical partitioning of the reconstructed samples after the difference by using the localization strategy. For the new input samples, the LTDGPR model with strong generalization ability is selected for integration, and the dynamic offset value of the dominant variable with certain time difference is estimated. Finally, based on the idea of TD model, the dominant variable value at the current time is predicted online. The validity and accuracy of the proposed method are verified by the data modeling and simulation of the debutane column process.
【作者單位】： 江南大學(xué)物聯(lián)網(wǎng)工程學(xué)院自動化研究所;輕工過程先進控制教育部重點實驗室;
【基金】：國家自然科學(xué)基金項目(21206053,21276111) 江蘇省“六大人才高峰”項目(2013-DZXX-043)~~
【分類號】：TQ018
，

本文編號：2336011