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Nonlinear Robust Identification with ε – GA: FPS Under Several Norms Simultaneously

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3512)

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.

Keywords

Multiobjective Optimization Optimization Problem Complexity Multimodal Problem IFAC Symposium Transfer Function Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and ControlPolytechnic University of Valencia 

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