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JITL (just-in-time learning) based multi-model fusion modeling method adopting GPR (Gaussian process regression)

A Gaussian process regression, real-time learning technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problems of soft sensor model prediction performance deterioration, etc., to reduce production costs, increase output, and improve product quality. Effect

Active Publication Date: 2015-06-10
JIANGNAN UNIV
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Problems solved by technology

Although the different stages of the chemical process can be effectively divided, in each operation stage, the time-varying process and equipment characteristics may change, which will deteriorate the predictive performance of the soft-sensing model

Method used

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  • JITL (just-in-time learning) based multi-model fusion modeling method adopting GPR (Gaussian process regression)
  • JITL (just-in-time learning) based multi-model fusion modeling method adopting GPR (Gaussian process regression)
  • JITL (just-in-time learning) based multi-model fusion modeling method adopting GPR (Gaussian process regression)

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Embodiment Construction

[0018] Combine below figure 1 Shown, the present invention is described in further detail:

[0019] Take the common chemical process - TE process as an example. Experimental data were obtained from the TE process to predict the content of component A in the predicted product stream.

[0020] Step 1: Collect input and output data to form a historical training database.

[0021] Step 2: use these training data to estimate the parameters of Gaussian mixture model (Gaussian mixture model, GMM). The complete input and output training data are then distributed to different stages of operation. Described GMM algorithm is:

[0022] GMM is a mixture of multiple Gaussian components, on the data X ∈ R n×m The probability density function of can be expressed as:

[0023] p ( X | Θ GM ) = Σ k = ...

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Abstract

The invention discloses a JITL (just-in-time learning) based multi-model fusion modeling method adopting GPR (Gaussian process regression). The method is used for a complex and changeful multi-stage chemical process and is a multi-model strategy which is continuously updated online; a Gaussian mixture model is adopted to identify different stages of the process, and a self-adaptive learning method is adopted to continuously update an established GPR model; when new data arrive, partially similar data are selected based on Euclidean distance and angle principle at each stage and used for establishing a partial GPR model; finally, new data obtained through calculation belong to posterior probability of each stage, and the partial model is subjected to fusion output. According to the method, key variables can be predicated accurately, so that the product quality is improved, and the production cost is reduced.

Description

technical field [0001] The invention relates to a Gaussian process regression multi-model fusion modeling method based on real-time learning, and belongs to the fields of complex industrial process modeling and soft measurement. 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 quality and reduce environm...

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Application Information

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IPC IPC(8): G06F17/50
Inventor 熊伟丽张伟薛明晨姚乐
Owner JIANGNAN UNIV
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