A new identification method for fuzzy linear models of non-linear dynamic systems

H.A.E. de Bruin, B. Roffel

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    21 Citations (Scopus)
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    The most promising methods for identifying a fuzzy model are data clustering, cluster merging and subsequent projection of the clusters on the input variable space. This article proposes to modify this procedure by adding a cluster rotation step, and a method for the direct calculation of the consequence parameters of the fuzzy linear model. These two additional steps make the model identification procedure more accurate and limits the loss of information during the identification procedure. The proposed method has been tested on a nonlinear first order model and a nonlinear model of a bioreactor and results are very promising.
    Original languageUndefined
    Pages (from-to)277-293
    Number of pages17
    JournalJournal of process control
    Issue number5
    Publication statusPublished - 1996


    • METIS-105703
    • IR-10519
    • Fuzzy linear model
    • model identification
    • fuzzy clustering

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