【摘要】：實際的工業(yè)過程往往具有多個不同的工況,為準確地對過程特性進行描述,多模型建模是一種有效的軟測量方法。通過對系統(tǒng)進行劃分,將復雜系統(tǒng)的建模問題簡化為局部建模問題,可以有效地提高軟測量模型的性能。多模型建模過程中,聚類方法、建模方法以及融合方式都會對軟測量模型的性能產(chǎn)生影響。因此本文從上述三個方面入手,以實際的工業(yè)生產(chǎn)過程為背景,對多模型軟測量建模方法進行研究。論文的主要研究內(nèi)容如下:針對具有多工況特性的復雜工業(yè)生產(chǎn)過程,對基于仿射傳播(Affinity Propagation,AP)聚類的高斯過程回歸(Gaussian Process Regression,GPR)軟測量建模方法進行研究。采用AP算法對訓練樣本進行類別劃分,得到不同工況下的子數(shù)據(jù)集,然后建立相應(yīng)的GPR局部預(yù)測模型,最后通過新來樣本到各子數(shù)據(jù)集聚類中心的距離計算得到各局部模型的權(quán)重,融合得到最終的預(yù)測模型�？紤]到過程數(shù)據(jù)維度較高的情況,提出一種基于改進AP的多模型軟測量方法。首先,采用主成分分析(Principal Component Analysis,PCA)方法和差分進化(Differential Evolution,DE)算法對AP算法進行改進,使算法可以避免冗余信息影響的同時,還可以實現(xiàn)參數(shù)的尋優(yōu),劃分得到全局最優(yōu)的子數(shù)據(jù)集;然后,建立各GPR局部預(yù)測模型;最后,對于新來的樣本,利用預(yù)測方差計算其隸屬于各局部模型的后驗概率,以此為權(quán)重對各局部模型進行融合,得到全局預(yù)測輸出。通過對兩個標準數(shù)據(jù)集和污水處理過程數(shù)據(jù)進行仿真,驗證了所提建模方法的有效性,對處理具有高維度特性的工業(yè)過程建模問題具有非常實用的參考價值。為解決模型性能隨時間推移而老化的問題,提出一種基于增量AP的在線軟測量建模方法,對軟測量模型和樣本數(shù)據(jù)庫進行及時更新。采用AP算法對訓練樣本進行劃分,對于新來的樣本,利用即時學習(Just-In-Time Learning,JITL)結(jié)合GPR的方法建立各局部預(yù)測模型,并進行融合得到在線的預(yù)測輸出;對新加入數(shù)據(jù)庫的樣本,用增量方法對AP算法進行改進,實現(xiàn)其證據(jù)矩陣的增量式更新,快速地完成對新來樣本的分類和數(shù)據(jù)庫的更新。通過對青霉素發(fā)酵過程數(shù)據(jù)進行建模仿真,驗證了所提在線軟測量方法的有效性。
[Abstract]:In order to accurately describe the characteristics of industrial processes, multi-model modeling is an effective soft sensing method. By dividing the system and simplifying the modeling problem of complex system into local modeling problem, the performance of soft sensor model can be improved effectively. In the process of multi-model modeling, clustering method, modeling method and fusion method will affect the performance of soft sensor model. Therefore, this paper starts with the above three aspects, taking the actual industrial production process as the background, and studies the multi-model soft sensor modeling method. The main contents of this paper are as follows: for the complex industrial production processes with multi-working conditions, the soft sensing modeling method of Gao Si process regression (Gaussian Process Regeneration based on affine propagation (AP) clustering is studied. The AP algorithm is used to classify the training samples, and the sub-data sets under different working conditions are obtained, and then the corresponding GPR local prediction model is established. Finally, the weight of each local model is obtained by calculating the distance from the new sample to the center of each sub-data cluster, and the final prediction model is obtained by fusion. Considering the high dimension of process data, a multi-model soft sensor method based on improved AP is proposed. Firstly, the principal component analysis (Principal Component) method and the Differential evolution (DE) algorithm are used to improve the AP algorithm, so that the algorithm can not only avoid the influence of redundant information, but also realize the optimization of parameters and partition the global optimal data set. Then, the local prediction models of each GPR are established. Finally, for the new samples, the posterior probability of each local model is calculated by using the prediction variance, which is used as the weight to fuse the local models to get the global prediction output. Through the simulation of two standard data sets and sewage treatment process data, the validity of the proposed modeling method is verified, and it has a very practical reference value for dealing with the industrial process modeling problems with high dimensional characteristics. In order to solve the problem of model performance aging with time, an on-line soft-sensor modeling method based on incremental AP is proposed, which updates the soft-sensor model and sample database in time. The AP algorithm is used to divide the training samples. For the new samples, the local prediction models are established by using the method of Just-In-Time learning (JITL) combined with GPR, and the online prediction output is obtained by fusion, and the new samples are added to the database. The incremental method is used to improve the AP algorithm to realize the incremental updating of its evidence matrix and to quickly complete the classification of the new samples and the updating of the database. Through modeling and simulation of penicillin fermentation process data, the effectiveness of the proposed online soft sensing method is verified.
【學位授予單位】：江南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP274

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