Strip steel exit thickness prediction method by symmetric extreme learning machine (Sym-ELM) optimized by random frog leaping algorithm

Abstract

The invention discloses a strip steel exit thickness prediction method by a symmetric extreme learning machine (Sym-ELM) optimized by a random frog leaping algorithm. Symmetry prior information is firstly added to the traditional extreme learning machine, the improved extreme learning machine can process a class of data containing symmetric properties, a symmetric extreme learning machine is put forward, the improved Sym-ELM algorithm is further optimized, the random frog leaping algorithm is used to select the input weight and the hidden layer bias value of the Sym-ELM algorithm, the optimized extreme learning machine is used for strip steel exit thickness prediction, and optimization on the symmetric extreme learning machine algorithm not only minimizes a root mean square error value but also considers a normal condition number of the output matrix of the hidden layer. The experiment shows that in comparison with the traditional extreme learning, the prediction performance of the traditional extreme learning machine to process the strip steel data containing the symmetric properties is improved, prediction errors of the model are reduced, and the generalization performance of the model is improved.

Description

technical field [0001] The invention relates to a method for predicting the strip exit thickness of a random leapfrog optimized symmetric extreme learning machine, and belongs to the technical field of strip exit thickness prediction. Background technique [0002] In the rolling production of the iron and steel industry, the accuracy of the exit thickness of the strip is an important evaluation criterion for the quality of the strip. Therefore, controlling the thickness of strip steel and improving the precision of strip steel thickness have become an important topic of general concern in the metallurgical industry at home and abroad. In the actual steel rolling process, the exit thickness accuracy of the strip will be affected by many aspects such as the entrance thickness, hardness, thermal expansion, and roll eccentricity of the raw material of the steel plate. By analyzing the data characteristics of strip steel rolling data, it is found that the actual values ​​of vari...

AI Technical Summary

Problems solved by technology

In other words, the various factors that affect the thickness of the strip outlet satisfy the symmetry law within this interval, and the traditional modeling methods often cannot effectively use these symmetry information

[0003] In recent years, the extreme learning machine algorithm has also been gradually applied to the prediction of rolling steel thickness. Due to the random setting of the initial network parameters, the training speed and learning ability of the network can be improved to a certain extent, but it is also due to the fact that the initial network parameters Given randomly, if the initial parameters of the extreme learning machine are not selected reasonably enough, the extreme learning machine will need more hidden layer nodes, which will affect its generalization

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