Nonlinear robust identification using multiobjective evolutionary algorithms

  • Authors:

  • J. M. Herrero;X. Blasco;M. Martínez;C. Ramos

  • Affiliations:

  • Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Polytechnic University of Valencia;Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Polytechnic University of Valencia;Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Polytechnic University of Valencia;Predictive Control and Heuristic Optimization Group, Department of Systems Engineering and Control, Polytechnic University of Valencia

  • Venue:
  • IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this article, a procedure to estimate a nonlinear models set (Θp) 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 ΘP 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.