Multi-objective genetic programming for nonlinear system identification

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Multi-objective genetic programming for nonlinear system identification

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Genetic programming is applied to the identification of non-linear polynomial models. This approach optimises multiple objectives simultaneously, and the solution set provides a trade-off between the complexity and the performance of the models. This is achieved using the concept of the non-dominated or Pareto-optimal solutions. The approach is tested on the simple Wiener model.

Inspec keywords: identification; genetic algorithms; nonlinear systems; polynomials

Other keywords: nonlinear system identification; polynomial model; nondominated solution; Pareto-optimal solution; Wiener model; multi-objective genetic programming

Subjects: Simulation, modelling and identification; Optimisation techniques

References

    1. 1)
      • R. Haber , H. Unbehauen . Structure identification of non-linear dynamic systems, a survey on input/output approaches. Automatica , 4 , 651 - 677
    2. 2)
      • Fonseca, C.M., Fleming, P.J.: `Genetic algorithms for multi-objective optimization: formulation, discussion and generalization', Proc. Fifth Int. Conf. on Genetic Algorithms, 1993, p. 416–423.
    3. 3)
      • J.R. Koza . (1992) Genetic programming: on the programming of computers by means of natural selection.
    4. 4)
      • I.J. Leontaris , S.A. Billings . Input–output parametric models for non-linear systems. Int. J. Control , 2 , 311 - 341
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