文章基本信息

标题：Practical Use of Savitzky-Golay Filtering-Based Ensemble Online SVR
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作者：Hiromasa Kaneko ; Takuya Matsumoto ; Shigeki Ootakara 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：7
页码：371-376
DOI：10.1016/j.ifacol.2016.07.364
语种：English
出版社：Elsevier
摘要：As a result of collaboration between Mitsui Chemicals, Inc. and the University of Tokyo, a soft sensor tool was developed and implemented in several plants in Mitsui Chemicals, Inc. A soft sensor is an inferential model constructed between process variables that are easy to measure (X) and process variables that are difficult to measure (y). y-values can be estimated in real time by inputting X-values into a soft sensor. To maintain predictive ability of a soft sensor to be high, we employ ensemble online support vector regression (EOSVR) model as an adaptive soft sensor model, which can adapt to both nonlinear changes and time-varying changes. Additionally, to reduce noise in estimated y-values, Savitzky-Golay (SG) filtering is used for estimated y-values. Our proposed method is called EOSVR-SG and implemented as a soft sensor tool. In this paper, we show our soft sensor tool used in real chemical plants and its execution results in which the EOSVR-SG model could estimate y-values accurately and smoothly.
关键词：Process controlSoft sensorSupport vector regressionAdaptive modelEnsembleNoise reductionSmoothingSavitzky-Golay filtering