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.
An Experimental Validation of a New Method for Multimodel Identification
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received October 3, 2016; final manuscript received July 14, 2017; published online October 6, 2017. Assoc. Editor: Yang Shi.
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Messaoud, A., and Abdennour, R. B. (October 6, 2017). "An Experimental Validation of a New Method for Multimodel Identification." ASME. J. Dyn. Sys., Meas., Control. February 2018; 140(2): 024502. https://doi.org/10.1115/1.4037530
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