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即时学习多模型加权GPR软测量方法

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即时学习多模型加权GPR软测量方法
A Soft Sensor Method Based on Just in Time Learning and Multi-Model Ensemble GPR
投稿时间:2016-07-07
DOI:10.15918/j.tbit1001-0645.2018.02.015
中文关键词:软测量即时学习集成建模高斯过程回归
English Keywords:soft sensorjust in time learningintegrated modelingGaussian process regression
基金项目:中国博士后科学基金资助项目(20100480208);山东省自然科学基金面上项目(ZR2016FM28)
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中文摘要:
针对工业过程中难以实现实时在线测量的重要过程变量,在主成分降解变量分组的基础上,提出了一种基于即时学习与集成学习的多模型高斯过程回归建模方法.该方法首先利用多变量组合实现集成模型的多样性,然后借助即时学习的自适应能力进行即时建模,最终多模型加权获得最终的预测结果.将所提方法应用于实际的工业炼胶过程,实验结果表明,该方法具有很好的预测性能.
English Summary:
For some difficulties to realize real-time measurements of vital variables, a novel soft sensor based on the just in time learning (JITL) and ensemble learning Gaussian process regression (GPR) modeling was proposed. Firstly, different sub-blocks were created by principal component decomposition. Then a set of JITL-GPR models were developed based on various sub-blocks. Finally, some better results from JITL-GPR models were combined to adaptively obtain the prediction result. The proposed soft sensor was applied to the industrial rubber producing process. Result verifies the superiority of the proposed method.
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