基于LSTM-JITRVM的冷轧轧制力建模方法研究

doi:10.3969/j.issn.1000-1158.2023.09.14

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Abstract In the process of strip cold rolling, the accuracy rolling force directly affects the product quality. In order to further improve the prediction accuracy of rolling force, a rolling force model based on LSTM-JITRVM (long short term memory- just in time relevance vector machine) is proposed. Firstly, the auto-encoding network is used to extract the deep-seated features of the input data. Then the local outlier factor algorithm is used to judge whether the test sample and its neighborhood points belong to the same distribution, and different self-learning regression models are used to fit different distributions. The simulation results show that the prediction accuracy of the model can be controlled within 3%, and the rolling force can be predicted online with high precision.

Key words： metrology rolling force prediction cold rolling machine learning cyclic self-coding network outlier factor algorithm

Received: 05 July 2022 Published: 21 September 2023

PACS:

TB931

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	Articles by authors
	SUN Hao
	ZHAO Ming-da
	LI Jing
	WEI Li-xin
	HU Zi-yu

Cite this article:

SUN Hao,ZHAO Ming-da,LI Jing, et al. Research on Modeling Method of Cold Rolling Force Based on LSTM-JITRVM[J]. Acta Metrologica Sinica, 2023, 44(9): 1409-1416.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2023.09.14 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2023/V44/I9/1409

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