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Nonlinear Robust Identification Using Multiobjective Evolutionary Algorithms

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 3562)

Abstract

In this article, a procedure to estimate a nonlinear models set (Θ p ) in a robust identification context, is presented. The estimated models are Pareto optimal when several identification error norms are considered simultaneously. A new multiobjective evolutionary algorithm \(\epsilon\nearrow - MOEA\) has been designed to converge towards Θ\(_{P}^{\rm \star}\), a reduced but well distributed representation of Θ P since the algorithm achieves good convergence and distribution of the Pareto front J(Θ). Finally, an experimental application of the \(\epsilon\nearrow - MOEA\) algorithm to the nonlinear robust identification of a scale furnace is presented. The model has three unknown parameters and ℓ ∞  and ℓ1 norms are been taken into account.

Keywords

  • Cost Function
  • Pareto Front
  • Optimal Pareto Front
  • Multiobjective Optimization Problem
  • Multiobjective Evolutionary Algorithm

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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  • DOI: 10.1007/11499305_24
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© 2005 Springer-Verlag Berlin Heidelberg

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Herrero, J.M., Blasco, X., Martínez, M., Ramos, C. (2005). Nonlinear Robust Identification Using Multiobjective Evolutionary Algorithms. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_24

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  • DOI: https://doi.org/10.1007/11499305_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26319-7

  • Online ISBN: 978-3-540-31673-2

  • eBook Packages: Computer ScienceComputer Science (R0)