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Soft measurement method based on half supervision learning

A semi-supervised learning and soft measurement technology, applied in the field of soft measurement instruments, can solve problems such as the inappropriate soft measurement model of semi-supervised Gaussian process classifiers, and achieve the effect of improving accuracy and wide application prospects.

Active Publication Date: 2015-04-22
SHANGHAI JIAOTONG UNIV
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Problems solved by technology

However, the modeling problem of a large number of industrial processes is not a classification problem, but belongs to the category of regression, so it is not appropriate to use a semi-supervised Gaussian process classifier as a soft sensor model in industrial processes

Method used

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  • Soft measurement method based on half supervision learning
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  • Soft measurement method based on half supervision learning

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Embodiment Construction

[0032] The embodiments of the present invention are described in detail below. This embodiment is implemented on the premise of the technical solution of the present invention, and detailed implementation methods and specific operating procedures are provided, but the protection scope of the present invention is not limited to the following implementation example.

[0033]

[0034]

[0035]

[0036] Output: Online prediction and computation of the mean and covariance of a Gaussian distribution of real-time data points.

[0037] The soft-sensing modeling process of flue gas oxygen content in a power plant is studied. Three technical indicators were used to evaluate the performance of the modeling method: relative root mean square error (RMSE), relative covariance tracking index (RVTP) and average log density error (LD).

[0038]

[0039] RMSE is mainly to evaluate the accuracy of the soft sensor model. The smaller the RMSE, the higher the accuracy; RVTP reflects wh...

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Abstract

The invention relates to a soft measurement method based on half supervision learning. The soft measurement method comprises the following steps of: firstly carrying out estimation on information of a geometric structure in a sample space by utilizing graphic Laplace to construct a covariance matrix in Gauss process regression on the basis of industrial process data and the half supervision learning; and introducing an unmarked sample to construct a half supervision core, and integrating the half supervision core with the Gauss process regression to construct a soft meter based on the half supervision, wherein key parameters of the soft meter can be determined by a cross validation manner; and finally realizing online update of the soft meter based on a rolling time window manner. According to the soft measurement method provided by the invention, disadvantages and defects in the conventional soft meter and soft measurement method are solved, an online Gauss process regression method based on the half supervision core is established by introducing the half supervision learning to an online process regression, and the soft meter is established by utilizing marked samples and unmarked samples so as to realize better prediction effect.

Description

technical field [0001] The invention relates to a method in the field of soft sensor technology, in particular to a soft sensor method based on semi-supervised learning. Background technique [0002] Soft instrument refers to a functional instrument that is difficult to measure certain variables in industrial process measurement, and uses other information obtained from direct physical sensor entities to obtain the required detection information through mathematical model calculations. Soft instrument technology is of great significance to the process industry and plays an important role in the field of modern process control. Soft sensor technology is already a very critical and cutting-edge technology in the field of modern process industry and process control. The existing soft instrumentation and soft measurement methods are all based on the foundation and framework of supervised learning methods. However, this technology can only use marked industrial data and samples...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F19/00
Inventor 阎威武张丹丹田宇
Owner SHANGHAI JIAOTONG UNIV