- 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, France",
- 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",
