Thermal process soft sensor modeling method based on least squares and support vector machine ensemble

Abstract

The invention discloses a thermal process soft sensor modeling method based on least squares and support vector machine ensemble, and belongs to the technical fields of thermal process and artificial intelligence intersection. The method includes selecting auxiliary variables as an input of a model and key variables to be predicted as an output of the model, selecting running data as an initial training sample, utilizing the soft fuzzy c-means clustering (SFCM) method to divide the initial sample into sub-datasets which are overlapped and which are provided with differences, establishing individual models on each sub-dataset, and synthesizing predicted outputs of the individual models to obtain estimation of the key variable; aiming to optional new acquired sample xk, obtaining a corresponding predicted value. According to the thermal process soft sensor modeling method, the soft fuzzy C-means clustering method is adopted, predicting accuracy is improved by means of establishing integrated models, calculating of the models is easier, and calculating efficiency is improved; boundary samples are processed effectively, the process is convenient to implement, the key variable can be predicted accurately, and important significance is provided to optimized operation of the thermal process system.

Description

technical field

[0001] The invention relates to a thermal process soft sensor modeling method based on least squares support vector machine (least squares support vector machine, LSSVM) integration, and belongs to the cross technical field of thermal technology and artificial intelligence. Background technique

[0002] Due to the limitations of detection technology and economic conditions, in the actual thermal process, some key variables such as composition and quality are difficult to achieve direct measurement. Establishing a relationship model between these variables and other variables is very important for realizing the optimization of the production process. has important meaning. Although some power stations have installed on-line analytical instruments to detect certain parameters, these hardware sensors are expensive, have high installation and maintenance costs, and are often shut down for maintenance. Therefore, it is necessary to construct soft sensor models of...

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