A NEW IDENTIFICATION METHOD FOR FUZZY LINEAR-MODELS OF NONLINEAR DYNAMIC-SYSTEMS

The most promising methods for identifying a fuzzy model are data clus tering, cluster merging and subsequent projection of the clusters on t he input variable space. This article proposes to modify this procedur e by adding a cluster rotation step, and a method for the direct calcu lation of the consequence parameters of the fuzzy linear model. These two additional steps make the model identification procedure more accu rate and limits the loss of information during the identification proc edure. The proposed method has been tested on a nonlinear first order model and a nonlinear model of a bioreactor and results are very promi sing.