Just-in-time learning soft measurement modeling method based on Bayes Gaussian mixture model

A hybrid model and modeling method technology, applied in character and pattern recognition, special data processing applications, instruments, etc., can solve the problem of inability to fully consider the non-Gaussian nature of process data, and achieve the effect of reducing production costs and improving product quality.

Inactive Publication Date: 2018-11-13
JIANGNAN UNIV
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Problems solved by technology

[0006] However, for some non-Gaussian time-varying industrial processes, the traditional JITL method selects similar data based on the Eucl

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  • Just-in-time learning soft measurement modeling method based on Bayes Gaussian mixture model
  • Just-in-time learning soft measurement modeling method based on Bayes Gaussian mixture model
  • Just-in-time learning soft measurement modeling method based on Bayes Gaussian mixture model

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Embodiment

[0056] This embodiment provides a real-time learning soft sensor modeling method based on the Bayesian Gaussian mixture model. This embodiment takes a common chemical process——butanizer process as an example. The experimental data comes from the debutanizer process to predict the butane concentration, see figure 1 , the method includes:

[0057] Step 1: Collect input and output data to obtain a historical training data set.

[0058] Step 2: Given the training sample X, use BIC to determine the optimal number K of Gaussian components. The description of BIC is as formula (1):

[0059] BIC=-2logp(X|Θ)+dlog(N) (1)

[0060] In formula (1), logp(X|Θ) represents the logarithmic likelihood function of the training samples, d represents the number of free parameters of the K Gaussian components, and N represents the number of training samples

[0061] Step 3: After obtaining the optimal number K of Gaussian components, given the initial parameters of the Gaussian mixture model (GM...

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Abstract

The invention discloses a just-in-time learning soft measurement modeling method based on a Bayes Gaussian mixture model, and belongs to the field of complex industrial process modeling and soft measurement. The method is used for a time varying industrial process with nonlinearity and non-Gaussianity; through a strategy of updating localities in real time online, a Bayes information criterion isadopted to determine an optimal Gaussian ingredient number; when new test data comes, a posterior probability that the new test data belongs to each Gaussian ingredient is calcuated, a Mahalanobis distance between the new test data and training data is solved, and the posterior probability and the Mahalanobis distance are blended to serve as a similarity index; and finally, one group of data withthe highest similarity is selected from the original training sample to establish a current GPR (Gaussian Process Regression) model, and model output prediction is carried out to achieve an effect onimproving product quality and lowering production cost.

Description

technical field [0001] The invention relates to an instant learning soft sensor modeling method based on a Bayesian Gaussian mixture model, which belongs to the fields of complex industrial process modeling and soft sensor. Background technique [0002] For some industrial processes that are nonlinear, time-varying and non-Gaussian, and the requirements for product quality in the process are constantly increasing, it is necessary to strictly monitor and control some process variables that directly determine product quality. However, due to the high price of some measuring instruments or the restriction of technical conditions, these variables cannot be measured by online instruments. [0003] In order to solve these problems, it is possible to estimate and predict by establishing a soft sensor model. Commonly used soft sensor methods include partial least squares (PLS), artificial neural networks (ANN), support vector machine ( support vector machine, SVM), etc. PLS can de...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06K9/62
CPCG06F30/20G06F18/24155G06F18/214
Inventor 熊伟丽祁成马君霞
Owner JIANGNAN UNIV
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