Abstract
Abstract Starting from a batch of experimental data, the modelling of dynamic processes, in terms of differential equations, has to achieve the estimation of parameter values by fitting the model to the data. Genetic Algorithms can be used for such purpose. The paper deals with two problems: to determine the parameter values without any initial guess, only specifying reasonable ranges for the values, and the rapid convergence to the values, when an initial guess is available. Two genetic-based methods, one for each problem, are presented. The second, very fast, can be used to track changes in process parameters.
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