Abstract
An algorithm for automatic selection and estimation of model parameters is presented. The algorithm uses a sensitivity matrix based calculation of the parameters effects on the measured outputs and of a linear-independence metric. A predictability degradation index and a parameter correlation degradation index are used as stop criteria and the method is extended to dynamic models and multiple operating points. The applicability of the developed algorithm is illustrated through a hypothetical nonlinear input-output model and through the analysis of data from an experimental isothermal batch bioreactor. The obtained results show the effectiveness of the algorithm.
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