Recursive fuzzy c-means clustering for recursive fuzzy identification of time-varying processes

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• 作者：Dejan Dovž ; an ; ^a ; ^{dejan.dovzan@fe.uni-lj.si} ; Igor &#x160 ; krjanc^a ; ^{igor.skrjanc@fe.uni-lj.si}
• 关键词：Recursive fuzzy ; none ; color ; black"" href=""/science?_ob=MathURL&_method=retrieve&_udi=B6V3P-5235HPF-1&_mathId=mml3&_user=578664&_cdi=5736&_pii=S001905781100005X&_rdoc=5&_issn=00190578&_acct=C000069537&_version=1&
• 刊名：ISA Transactions
• 出版年：2011
• 出版时间：April 2011
• 年：2011
• 卷：50
• 期：2
• 页码：159-169
• 全文大小：1280 K

文摘

In this paper we propose a new approach to on-line Takagi–Sugeno fuzzy model identification. It combines a recursive fuzzy c-means algorithm and recursive least squares. First the method is derived and than it is tested and compared on a benchmark problem of the Mackey–Glass time series with other established on-line identification methods. We showed that the developed algorithm gives a comparable degree of accuracy to other algorithms. The proposed algorithm can be used in a number of fields, including adaptive nonlinear control, model predictive control, fault detection, diagnostics and robotics. An example of identification based on a real data of the waste-water treatment process is also presented.