Acta Metrologica Sinica  2019, Vol. 40 Issue (1): 111-116    DOI: 10.3969/j.issn.1000-1158.2019.01.18
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Online Cold Rolling Prediction Based on Improved OS-ELM
WEI Li-xin,ZHANG Yu,SUN Hao,WEI Xin-yu
Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University,Qinhuangdao, Hebei 066004, China
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Abstract  The cold rolling force prediction results directly affect the rolling precision and product quality of the plate (belt). Because of the complicated cold rolling process and strong coupling of parameters, the model is not easy to be established and the actual deviation is large. An improved on-line prediction method of online sequential extreme learning machine is proposed. Using quantum behaved particle swarm optimization algorithm to optimize the weights and thresholds, according to the contribution of the hidden layer to the network output in the current training data, the topology structure of the network is adjusted and realized the self-organization of the structure and parameters. The experimental results show that the self-organizing online sequential extreme learning machine has a higher improvement in the training speed and precision than the artificial bee colony optimization in the reverse propagation neural network and incremental extreme learning machine based on the enhanced random search.
Received: 20 September 2017      Published: 07 January 2019
PACS:  TB931  
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WEI Li-xin
ZHANG Yu
SUN Hao
WEI Xin-yu
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WEI Li-xin,ZHANG Yu,SUN Hao, et al. Online Cold Rolling Prediction Based on Improved OS-ELM[J]. Acta Metrologica Sinica, 2019, 40(1): 111-116.
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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.01.18     OR     http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I1/111
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