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
CN104699894AActive Publication Date: 2015-06-10JIANGNAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
JIANGNAN UNIV
Publication Date
2015-06-10

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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.
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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...

Claims

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