Plate convexity prediction method based on kernel partial least squares (KPLS) and support vector machine combined

Abstract

The invention belongs to the technical field of convexity prediction, and particularly relates to a plate convexity prediction method based on kernel partial least squares (KPLS) and a support vectormachine combined. The plate convexity prediction method comprises the following steps: S1, field data are collected through a high-precision monitoring device; S2, the collected data are preprocessed;S3, a KPLS regression prediction model is established; and S4, a KPLS-SVM plate convexity prediction model is established. By taking a data-driven algorithm as a mathematical tool, abnormal values can be removed from a large quantity of collected field rolling process data, a convexity prediction model for a strip steel continuous-rolling plate based on a KPLS method and the support vector machine combined is established, the convexity of the strip steel continuous-rolling plate is predicted, the established model is optimized through a particle swarm optimization algorithm, and the prediction precision of the convexity of the strip steel continuous-rolling plate is further improved. The plate convexity prediction method is used for predicting the convexity of the strip steel continuous-rolling plate.

Description

technical field [0001] The invention belongs to the technical field of convexity prediction, and in particular relates to a board convexity prediction method based on kernel partial least squares combined with a support vector machine. Background technique [0002] The plate shape refers to the appearance shape of the plate and strip, which is one of the important indicators to measure the geometric accuracy of the plate and strip, just like the thickness and width. The plate shape actually includes two aspects: the geometric shape of the strip steel section and the flatness of the strip in the natural state. Therefore, quantitative description of the plate shape involves multiple indicators such as convexity, wedge shape, edge weakening, local high point and flatness. , where the plate crown is the main factor to describe and measure the hot-rolled plate shape. The problem of flatness quality has always been the focus of researchers. With the increasing requirements for fl...

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Problems solved by technology

[0003] Aiming at the technical problem of insufficient accuracy of the above-mentioned general flatness control method, the present invention provides a rolling strip convexity prediction method based on kernel partial least square method combined with support vector machine, which is simple to operate, easy to implement and high in precision.

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