Contribution a l'Identification de Systemes non-Lineaires en Milieu Bruite pour la Modelisation de Structures Mecaniques Soumises a des Excitations Vibratoires

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@PhdThesis{SIGRIST_ZOE_2012,
  author =       "Zoe Laure Malika Sigrist",
  title =        "Contribution a l'Identification de Systemes
                 non-Lineaires en Milieu Bruite pour la Modelisation de
                 Structures Mecaniques Soumises a des Excitations
                 Vibratoires",
  school =       "Laboratoire IMS, UMR CNRS 5218, University of
                 Bordeaux",
  year =         "2012",
  address =      "351 cours de la Liberation, 33405 Talence Cedex",
  month =        "4 " # dec,
  keywords =     "genetic algorithms, genetic programming, Volterra
                 series, NARX model, NOE model, estimation bias, EIV,
                 LMS algorithm, algorithms without estimation bias,
                 differential evolution algorithms",
  URL =          "http://ori-oai.u-bordeaux1.fr/pdf/2012/SIGRIST_ZOE_2012.pdf",
  size =         "201 pages",
  abstract =     "This PhD deals with the characterisation of mechanical
                 structures, by its structural parameters, when only
                 noisy observations disturbed by additive measurement
                 noises, assumed to be zero-mean white and Gaussian, are
                 available. For this purpose, we suggest using
                 discrete-time models with distinct linear and nonlinear
                 parts. The first one allows the structural parameters
                 to be retrieved whereas the second one gives
                 information on the nonlinearity. When dealing with
                 non-recursive Volterra series, we propose an
                 errors-in-variables (EIV) method to jointly estimate
                 the noise variances and the Volterra kernels. We also
                 suggest a modified unbiased LMS algorithm to estimate
                 the model parameters provided that the input-noise
                 variance is known. When dealing with recursive
                 polynomial model, we propose two methods using
                 evolutionary algorithms. The first includes a stop
                 protocol that takes into account the output-noise
                 variance. In the second one, the fitness functions are
                 based on correlation criteria in which the noise
                 influence is removed or compensated.",
  notes =        "

                 In french. Automatique, Productique, Signal et Image,
                 Ingenierie Cognitique",
}

Genetic Programming entries for Zoe Sigrist

Citations