In this paper, we propose a new method for an optimal systematic determination of models' base for multimodel identification. This method is based on the neural classification of data set picked out on a considered nonlinear system. The obtained cluster centers are exploited to provide the weighting functions and to deduce the corresponding dispersions and their models' base. A simulation example and an experimental validation on a chemical reactor are presented to evaluate the effectiveness of the proposed method.

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