A method for signal pattern recognition of cold-rolled strip

Abstract

A method for signal pattern recognition of cold-rolled strips, the method comprising the following steps: collecting the flatness measurement values ​​of each measurement section in the width direction of the cold-rolled strip measured online by a shapemeter, and obtaining the flatness value of each measurement section; The original data output by the shape meter is input to an n-layer neural network as the feature extraction layer, mainly through training to let the network automatically extract features to eliminate artificial traces; use the improved quantum neural network based on genetic algorithm for plate shape recognition. The invention applies the improved quantum neural network of the multi-layer excitation function optimized by the genetic algorithm to the shape pattern recognition technology, which significantly improves the training efficiency of the network and effectively solves the problems of accuracy and real-time performance encountered in the traditional shape recognition method It is not ideal enough, the network structure is complex, the training time is long, and the stability and robustness are poor.

Description

technical field [0001] The invention belongs to the field of cold-rolled strips and relates to a method for identifying signal patterns of cold-rolled strips. Background technique [0002] In the production process of cold-rolled strips, the pattern recognition of cold-rolled strip shape is an important part of the cold-rolled strip shape control system. In this link, the recognition accuracy of the flatness defect state is crucial to the subsequent flatness control effect. role. The stress signal data in the plate on the production site is collected by the shape meter for identification, and the type of the current shape defect state is judged, and fed back to the shape control mechanism, and the defect degree of the shape is reduced by controlling the adjustment of the actuator, and finally To make the output of the shape meet the production standard of the process, it is necessary to seek and study the identification method of the high-precision shape defect mode. [00...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a method for signal pattern recognition of cold-rolled strips, which can effectively solve the problems of insufficient accuracy and real-time performance encountered in traditional flatness pattern recognition methods, complex identification model structure and too long network training time , poor stability and other technical problems can provide a reliable control basis for the control system and provide a strong guarantee for improving the quality of cold-rolled strip shape control

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