Method for Predicting Third Octave Chatter of High Speed ​​Cold Rolling Mill Based on BP Neural Network

Abstract

A high-speed cold-rolling mill third octave flutter prediction method based on a BP neural network is a method for predicting the machine frame vibration situation in the rolling process of a high-speed cold-rolling mill by utilizing the neural network and belongs to the field of material engineering computer neural network techniques. An additional momentum method and a conjugate gradient method are introduced to improve a BP neural network prediction mode. By adopting the additional momentum method, the network local minimum problem in rational neural network application can be solved, the algorithmic accuracy can be effectively improved, and the network leaning capacity can be improved. Due to the fact that the number of network parameters is large and considering the problems of large storage capacity and low convergence rate, the conjugate gradient method is selected, and good training effect is obtained. The high-speed cold-rolling mill third octave flutter prediction method is effectively applied to third octave flutter prediction of the high-speed cold-rolling mill and used for guiding production.

Description

technical field [0001] The invention relates to a BP neural network-based method for predicting third-octave flutter of a high-speed cold rolling mill, a method for predicting frame vibration during the rolling process of a high-speed cold rolling mill by using a neural network, and belongs to material engineering computer neural network Network technology control field. Background technique [0002] Steel plays an important role in the development of the national economy. Steel is widely used in industrial production, transportation, national defense, aerospace and other major fields. Vibration problems in the operation of rolling mills are one of the main obstacles limiting the improvement of output and quality in the industry. Predicting the vertical vibration of the rolling mill is of great significance to the thickness and precision of the strip, and to improving the production quality of steel products. It also has a very good guiding effect on improving the product...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to solve the problem that the traditional BP neural network algorithm has local minimum and slow convergence speed, reduce the influence of the third octave flutter on the quality of rolled products, and provide a high-speed cold rolling mill based on BP neural network Prediction method for third octave flutter

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