基于烟花算法与长短时记忆网络的模温预测方法研究

doi:10.3969/j.issn.1000-1158.2020.06.02

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Abstract Aiming at the problems of low precision of mold temperature and difficulty in selecting network parameters, based on fireworks algorithm to optimize the long and short time memory network, a mold temperature prediction model is proposed, which provides a basis for automatic control of casting mold temperature. Firstly, according to the production casting process, the main variables affecting the casting system are selected. The grey relational analysis is used to obtain the grey correlation degree of each variable and remove the small correlation degree, and the data set of the mold temperature influence factor variable is established. Secondly, the fireworks algorithm is used to optimize the long and short time memory network to establish a mold temperature prediction model. Finally, the prediction results are compared with BP neural network and long-term short-term networks. Experiments show that the absolute error of the model temperature prediction method based on the long-term and short-term memory network optimized by the firework algorithm is less than 2.4℃ and the mean absolute percentage error is less than 0.12.

Key words： metrology mold temperature gray correlation analysis fireworks algorithm long and short time memory network

Received: 10 December 2019 Published: 08 June 2020

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TB942

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	Articles by authors
	ZHANG Dian-fan
	LI Zi-hao
	CHENG Shu-hong

Cite this article:

ZHANG Dian-fan,LI Zi-hao,CHENG Shu-hong. Research on Model Temperature Prediction Method Based on Fireworks Algorithm and Long and Short Time Memory Network[J]. Acta Metrologica Sinica, 2020, 41(6): 640-645.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.06.02 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I6/640

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