Systems identification with fractional models using noisy input output data
type: Dataset
description: This thesis deals with continuous-time system identification by fractional models in the EIV context. Two classes of methods are developed : the first class is based on third-order statistics and the second one is based on fourth-order statistics. Firstly, all differentiation orders are known a priori and only the coefficients of the differential equation are estimated using the developed algorithms based on higher-order statistics. Then, they are extended to estimate both the fractional differential equation coefficients and the commensurate order. Simulation examples display the theoretical developments on system identification in the EIV context. A practical application for modeling heat transfer phenomena in an aluminium rod and for modeling an electronic real system have shown the efficiency of the developed methods.
identifier: NNT: 2013BOR15244
issued: 2026-05-05T11:35:14.593018
language: fr
modified: 2026-05-05T11:35:14.593023
contact point: Organization
  fn: CCSD
keyword: continuous-time
keyword: derivation-non-entiere
keyword: erreurs-en-les-variables
keyword: errors-in-variables
keyword: fractional-differentiation
keyword: higher-order-statistics
keyword: identification
keyword: infoeu-reposemanticsdoctoralthesis
keyword: spiotherengineering-sciences-physicsother
keyword: statistiques-dordre-superieur
keyword: system-identification
keyword: temps-continu
keyword: theses
landing page: https://theses.hal.science/tel-00989549

Systems identification with fractional models using noisy input output data
type: Distribution
format: HTML
issued: 2026-05-05T11:35:14.600828
modified: 2026-05-05T11:35:14.573337
access URL: https://theses.hal.science/tel-00989549

test_moissonnage_selune
type: Agent