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作 者:刘晶晶 贺凯迅 LIU Jing-jing;HE Kai-xun(College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,China;Ministry of Education of Advanced Chemical Process Control and Optimization Technology,East China University of Science and Technology,Shanghai 200237,China)
机构地区:[1]山东科技大学电气与自动化工程学院,山东青岛266590 [2]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237
出 处:《控制工程》2022年第10期1887-1892,共6页Control Engineering of China
基 金:国家自然科学基金资助项目(61803234,61751307);中央高校基本科研业务费专项资金资助项目。
摘 要:根据工况需求建立稳健性好的定量分析模型是使用近红外分析技术的关键。近红外光谱的波长点数多,实际工况中难以获得精确的无偏估计,可控的有偏估计更便于后续优化控制对产品目标属性的调优。鉴于此,提出一种融合互信息的光谱波长选择方法:根据光谱波长间的最大信息系数,选择与目标属性相关性大的波长,实现有监督波长选择。此外,融合即时学习,以有偏最小最大概率机建立定量分析模型,在保证有偏估计的同时提高了模型的自适应能力。工业案例验证结果表明,所提方法可有效提高近红外定量分析模型的精度,减少累积偏差。The key of near-infrared analysis technology is to establish a robust quantitative analysis model according to the corresponding process conditions.It is difficult to obtain an accurate unbiased estimation in real-world because there are a large number of wavelength points in the near-infrared spectrum.The controllable biased estimation is more convenient for Optimization of product target attributes by optimization control.In view of this,a spectral wavelength selection method based on mutual information is proposed.According to the maximum information coefficient between the spectral wavelengths,the wavelength which has high correlation with the target attribute is selected.In this way,supervised wavelength selection is realized.Besides,the biased minimax probability machine combined with just-in-time learning approach is adopted to establish the quantitative analysis model,which improves the adaptive ability of the model while performing the biased estimation The industrial case shows that the proposed method can effectively improve the accuracy of near-infrared quantitative analysis model and reduce the cumulative error.
关 键 词:近红外光谱 最小最大概率机 互信息 波长选择 汽油调合
分 类 号:TP18[自动化与计算机技术—控制理论与控制工程]
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