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| Flatness pattern recognition model based on recurrent neural network |
| SONG Ming-ming1，WANG Dong-cheng1, 2，ZHANG Shuai1，XU Yang-huan1，LIU Hong-min1, 2 |
（1. National Engineering Research Center for Equipment and Technology of Cold Rolling Strip，Yanshan University，Qinhuangdao 066004， Hebei， China
2. State Key Laboratory of Metastable Materials Science and Technology，Yanshan University， Qinhuangdao 066004， Hebei， China ） |
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Abstract The flatness pattern recognition is the key link of the flatness control. The traditional flatness pattern recognition has some shortcomings，such as the poor recognition precision and the poor anti-interference ability. With the complexity of the data regression tasks increasing，the classification algorithm based on the deep learning has been used for many tasks such as the data classification， the image processing，the pattern recognition and the feature extraction. A deep learning can achieve a complex function approximation by learning a kind of deep nonlinear network structure. Based on this background，a flatness pattern recognition model based on the recurrent neural network was proposed. The results showed that the flatness pattern recognition model based on RNN could achieve the training of large flatness data，and the recognition accuracy and the generalization ability of the model were very high. It provides a new method for the further improvement of the accuracy of the flatness control.
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Received: 26 March 2018
Published: 20 November 2018
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2018, Vol. 53 
