Abstract
In nonlinear robust identification context, a process model is represented by a nominal model and possible deviations. With parametric models this process model can be expressed as the so-called Feasible Parameter Set (FPS), which derives from the minimization of identification error specific norms. In this work, several norms are used simultaneously to obtain the FPS. This fact improves the model quality but, as counterpart, it increases the optimization problem complexity resulting in a multimodal problem with an infinite number of minima with the same value which constitutes FPS contour. A special Evolutionary Algorithm (ε– GA) has been developed to find this contour. Finally, an application to a thermal process identification is presented.
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Herrero, J.M., Blasco, X., Martínez, M., Ramos, C. (2005). Nonlinear Robust Identification with ε – GA: FPS Under Several Norms Simultaneously. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_122
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DOI: https://doi.org/10.1007/11494669_122
Publisher Name: Springer, Berlin, Heidelberg
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