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Alternative TitleApplication of Case-based Reasoning and Iterative Learning to Laminar Cooling Process Control
片锦香; 柴天佑; 李界家
Source Publication自动化学报
Indexed ByEI ; CSCD
EI Accession number20130315915473
Contribution Rank1
Funding Organization国家重点基础研究发展计划(973计划)(2009CB320601);国家自然科学基金(61104084,61174164);住房和城市建设部科学技术计划项目(2012-K7-19)资助
Keyword层流冷却 卷取温度 案例推理 迭代学习
Abstract现有的卷取温度预报补偿模型和带钢批次间补偿模型中,由于案例推理(Case-based reasoning,CBR)系统中检索特征权重系数采用人工凑试的方法,难以获得满意的补偿作用,且由于缺乏迭代学习的初始工况条件的匹配算法,难以进行准确匹配和有效迭代.因此,本文针对这两个问题,提出了基于神经网络技术的案例推理系统检索特征权重系数自动学习算法及迭代学习技术初始工况匹配算法,改进了卷取温度预报补偿模型和带钢批次间补偿模型,并采用国内某大型钢厂的现场实际数据进行实验研究.实验结果表明,与原有方法相比,带钢卷取温度的控制偏差减小了1.63C,卷取温度精度控制在±10C以内的命中率提高了14.5%.
Other AbstractIn some previous study, the strip coiling temperature prediction compensator and batch to batch compensator cannot obtain good compensating results due to the manually adjusted weight parameters for index feature of the case-based reasoning (CBR) system. And exact match and effective iteration cannot be done for the lack of initial operating condition matching algorithm. For this reason, a method based on neural network technology is proposed to learn the weights parameters of the index features of CBR system, with an initial operating condition matching algorithm that uses iterative learning technique to improve prediction compensator and the batch to batch compensator. The proposed hybrid intelligent control method is applied to a large domestic steel plant, and the results show that the strip coiling temperature control error decrease 1.63°C and the hit rate increased 14.5 % where the coiling temperature errors are controlled in the range of ±10°C.
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Cited Times:6[CSCD]   [CSCD Record]
Document Type期刊论文
Recommended Citation
GB/T 7714
片锦香,柴天佑,李界家. 案例推理及迭代学习在层流冷却控制中的应用[J]. 自动化学报,2012,38(12):2032-2037.
APA 片锦香,柴天佑,&李界家.(2012).案例推理及迭代学习在层流冷却控制中的应用.自动化学报,38(12),2032-2037.
MLA 片锦香,et al."案例推理及迭代学习在层流冷却控制中的应用".自动化学报 38.12(2012):2032-2037.
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