Nonlinear Robust Identification Using Multiobjective Evolutionary Algorithms
نویسندگان
چکیده
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 ↗−MOEA has been designed to converge towards Θ P , 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 ↗−MOEA algorithm to the nonlinear robust identification of a scale furnace is presented. The model has three unknown parameters and ∞ and 1 norms are been taken into account.
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