CNN algorithm and Lasso regression model-based hot-rolling product quality prediction method

Abstract

The invention belongs to the technical field of steel rolling product quality prediction, and particularly relates to a CNN algorithm and Lasso regression model-based hot rolling product quality prediction method. The method comprises the following steps of S1, obtaining the sample data used for modeling, wherein the sample data comprise the training data, and determining the key input variables; S2, training the CNN by using the key input variables of the sample data to obtain a feature vector model; S3, substituting the key input variables of the training data into the feature vector model to obtain an input variable for substituting into the Lasso regression model; S4, determining an optimal regularization factor of the Lasso regression model, training the Lasso regression model by utilizing the input variables in the step S3 to obtain an uncorrected mixed prediction model, and correcting the uncorrected mixed prediction model to obtain a corrected mixed prediction model; and S5, inputting the production data in the future time period into the corrected mixed prediction model to obtain a prediction result of the production data. The method can improve the prediction precision of the model.

Description

technical field [0001] The invention belongs to the technical field of quality prediction of rolled steel products, in particular to a method for predicting the quality of hot-rolled products based on a CNN algorithm and a Lasso regression model. Background technique [0002] In recent years, although iron and steel enterprises have solved the problem of insufficient production of iron and steel products, they do not have a suitable control model for product quality, resulting in higher than average resource usage in the production process and excessive energy consumption in rolling steel production. Compared with technology, there is still a lot of room for improvement. At present, steel products are mainly used in the construction industry, aerospace, and automobile manufacturing. These industries have very strict requirements on steel quality. Therefore, a suitable and accurate quality prediction model for rolled steel products is established to predict product quality an...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the existing technical problems, the present invention provides a method for predicting the quality of hot-rolled products based on the CNN algorithm and the Lasso regression model that can alleviate the problem of model overfitting to a certain extent and improve the prediction accuracy of the model

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