Soft measurement modeling method based on semi-supervised ensemble learning

A technology that integrates learning and modeling methods, applied in character and pattern recognition, instruments, computer components, etc., can solve the problems of inaccurate prediction of key variables and low performance of model prediction, so as to improve product quality and reduce production costs Effect

Active Publication Date: 2018-11-06
合肥名龙电子科技有限公司
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Problems solved by technology

[0005] In order to solve the problem that the acquisition frequency of dominant variables is far lower than that of auxiliary variables in the actual industrial process, the soft sensor modeling method based on semi-supervised

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  • Soft measurement modeling method based on semi-supervised ensemble learning
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  • Soft measurement modeling method based on semi-supervised ensemble learning

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Embodiment 1

[0032] This embodiment combines a common chemical process-the debutanizer process as an example, see figure 1 The experimental data comes from the process of the debutanizer E, and the soft sensor modeling method based on semi-supervised integrated learning provided by the present invention is used to predict the butane concentration:

[0033] Step 1: The collection process has label sample set L={X L ,Y L }, L means labeled; and unlabeled sample set U={X U }, U means unlabeled, and for the unlabeled sample set U, the Bagging algorithm is used to generate three unlabeled sample subsets U 1 , U 2 , U 3 .

[0034] Step 2: Use a subset of labeled samples to establish an initial GPR model, f i =GPR(L i ), the initial subset of labeled samples L i =L,i=1, 2, 3.

[0035] Step 3: For U 1 Each sample in x u , Using the nearest neighbor method from L 2 And L 3 Select num samples that are close to each other to obtain the nearest neighbor sample set Ω 2 And Ω 3 .

[0036] Step 4: Use f as in for...

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Abstract

The invention discloses a soft measurement modeling method based on semi-supervised ensemble learning, belongs to the field of modeling and soft measurement in a complex industrial process, and is used for a chemical process with a small quantity of label samples. The method is an online prediction strategy based on semi-supervision. A Bagging algorithm is adopted to divide a no-label sample set into three sub sample sets, and the label sample is adopted to train three regression models; then, on the basis of a confidence coefficient index, the corresponding index value of the no-label sampleis calculated, the no-label sample which meets a confidence coefficient requirement is selected for carrying out labeling, and the labeled sample is added into the corresponding label sub sample set;and finally, a Gaussian process regression model is independently established for three enlarged label datasets, and a weighting method is adopted for carrying out result fusion. By use of the method,the no-label sample information in the chemical process can be effectively utilized to accurately predict a key variable so as to improve product quality and lower production cost.

Description

Technical field [0001] The invention relates to a soft measurement modeling method based on semi-supervised integrated learning, which belongs to the field of complex industrial process modeling and soft measurement. Background technique [0002] Some important quality variables in industrial processes such as chemical industry, metallurgy and fermentation are often unable to be measured by online instruments, and there is a serious lag in the way of offline analysis in the laboratory. The data-based soft-sensing modeling method does not require in-depth knowledge of the process mechanism, and has the advantages of low maintenance cost and low measurement delay. In recent years, it has been widely used in industrial process modeling. The traditional soft-sensing modeling method only considers the information of labeled samples in the industrial process and discards a large number of unlabeled samples. However, in the actual process, the number of labeled samples is far less than ...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2155
Inventor 熊伟丽
Owner 合肥名龙电子科技有限公司
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