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Semi-supervised Gaussian process regression soft measurement modeling method improving self-training algorithm

A Gaussian process regression and modeling method technology, applied in the field of semi-supervised Gaussian process regression soft sensor modeling, can solve the problems of indistinguishable estimated values ​​and missing leading variable sample estimates

Active Publication Date: 2017-12-08
JIANGNAN UNIV
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Problems solved by technology

[0006] The traditional self-training algorithm only realizes the estimation of missing leading variable samples, but cannot distinguish the quality of the estimated value

Method used

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  • Semi-supervised Gaussian process regression soft measurement modeling method improving self-training algorithm
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  • Semi-supervised Gaussian process regression soft measurement modeling method improving self-training algorithm

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Embodiment Construction

[0014] Combine below figure 1 Shown, the present invention is described in further detail:

[0015] Take the common chemical process—debutanizer process as an example. The experimental data come from the debutanizer E process to predict the butane concentration.

[0016] Step 1: Collect a labeled sample set {X L ,Y L}, L means labeled; and unlabeled sample set {X U}, U means unlabeled, and for each sample x in the unlabeled sample set i ∈{X U}, calculate its relationship with each sample x in the labeled sample set j ∈{X L ,j=1,2,...,N L} similarity, N L Indicates the number of labeled samples, the calculation of the similarity index Sim is shown in formula (1), and the similarity in descending order is recorded as RSim, where γ∈(0,1) is the similarity parameter, ||x i -x j ||, cosi ,x j >respectively represent the vector x i ,x j The Euclidean distance between and the cosine of the included angle.

[0017] Sim j =γexp(-||x i -x j ||)+(1-γ)cosi ,x j > (1)

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Abstract

The invention discloses a semi-supervised Gaussian process regression soft measurement modeling method on the basis of improving a self-training algorithm. The method is used for chemical process soft measurement modeling of a training dataset with a deletion primary variable. The method comprises the steps that deleted primary variable samples are estimated through the self-training algorithm, according to the influence of obtained estimation samples on original training data, samples high in generalization capacity are screened out and added into an original sample set, and then a new training sample set is formed to conduct modeling. By means of the method, on one hand, effective screening of estimation samples is achieved, and the semi-supervised model precision is improved; on the other hand, screening rules are simple, an entire data set does not need to be divided, and limitation of the model structure does not exist. Accordingly, the product quality can be improved, and the production cost can be lowered.

Description

technical field [0001] The invention relates to a semi-supervised Gaussian process regression soft sensor modeling method based on an improved self-training algorithm, and belongs to the fields of complex industrial process modeling and soft sensor. Background technique [0002] At present, the complexity of the chemical process is increasing day by day, and the requirements for product quality are also constantly improving. Modern industries often need to be equipped with some advanced monitoring systems. However, some important process variables cannot be measured effectively in real time due to the disadvantages of high price, poor reliability or large measurement hysteresis of sensors for some key quality variables. [0003] In order to solve these problems, soft sensing technology has received more and more attention in the field of industrial processes. In the past ten years, data-driven soft sensor modeling technology has been widely studied to improve product qualit...

Claims

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Application Information

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IPC IPC(8): G06F17/18G06K9/62
CPCG06F17/18G06F18/214
Inventor 熊伟丽史旭东
Owner JIANGNAN UNIV
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