期刊
CONTROL ENGINEERING PRACTICE
卷 15, 期 7, 页码 769-778出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2005.12.005
关键词
nonlinear system modeling; principal component analysis; fuzzy c-means; fuzzy Takagi-Sugeno method; recursive least squares
Since online measurement of the melt index (MI) of polyethylene is difficult, a virtual sensor model is desirable. However, a polyethylene process usually produces products with multiple grades. The relation between process and quality variables is highly nonlinear. Besides, a virtual sensor model in real plant process with many inputs has to deal with collinearity and time-varying issues. A new recursive algorithm, which models a multivariable, time-varying and nonlinear system, is presented. Principal component analysis (PCA) is used to eliminate the collinearity. Fuzzy c-means (FCM) and fuzzy Takagi-Sugeno (FTS) modeling are used to decompose the nonlinear system into several linear subsystems. Effectiveness of the model is demonstrated using real plant data from a polyethylene process. (c) 2006 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据