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Monte Carlo cross verification-based multiple-soft sensing algorithm cluster modeling method

A technology of cross-validation and modeling methods, which is applied in the field of multiple soft sensor algorithm cluster modeling to achieve the effect of improving accuracy

Inactive Publication Date: 2017-06-27
ZHEJIANG UNIV
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[0047] In order to overcome the outlier problem, the present invention systematically applies the model evaluation and outlier search aspects of the model cluster analysis method, and attempts to construct an integrated learning system under the framework of the model cluster analysis method. In terms of finding outliers, the Monte Carlo cross-validation algorithm MC based on the model cluster analysis method is used. After eliminating outliers, a variety of soft sensor algorithms are selected to model the industrial process, and then the prediction of each soft sensor algorithm is performed. The results are integrated. In terms of model evaluation, by generating a large number of training clusters, the influence of the selection of the training set on the model evaluation results is eliminated, and the diversity of data is improved.

[0048] The effectiveness of the present invention is illustrated below in conjunction with the example of a specific industrial process. In th...

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Abstract

The invention discloses a Monte Carlo cross verification-based multiple-soft sensing algorithm cluster modeling method. According to the method, the model evaluation and outlier search of a model cluster analysis method are systematically applied. The invention tries to construct an ensemble learning system under the framework of the model cluster analysis method. In the outlier search, a model cluster analysis method-based Monte Carlo cross validation algorithm is adopted; after outliers are removed, a variety of soft-sensing algorithms are selected to model industrial processes; and the prediction results of the soft-sensing algorithms are integrated. In the model evaluation, a large number of training clusters are generated, so that influence on a model evaluation result caused by the selection of the training clusters can be eliminated, and therefore, the diversity of data is improved. Compared with existing other methods, the method of the invention can improve the accuracy of prediction by removing the outliers and analyze the change of the overall prediction effects of the algorithms before and after removing the outliers from the perspective of statistics.

Description

technical field [0001] The invention belongs to the field of industrial process control, in particular to a cluster modeling method of multiple soft sensor algorithms based on Monte Carlo cross-validation. Background technique [0002] In recent years, due to the efficiency and quality requirements of industrial production, soft sensing has become an important research field. In chemical, fermentation, biological, metallurgy, petroleum, food and other process industries, many important processes need to be strictly controlled in order to achieve card edge control, make the production equipment run in the best working condition, and produce more high-quality products. variable. However, it is often difficult to directly measure these important process variables with online sensors, so the method of soft measurement has appeared. [0003] Before soft sensor modeling, it is necessary to perform data preprocessing, such as data normalization, outlier removal, etc. Normalizati...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N7/00G06N3/04G06N3/08
CPCG06N3/08G06N7/01G06N3/045
Inventor 葛志强陆建丽
Owner ZHEJIANG UNIV
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