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1	Les généralisations des récursivités de Kalman et leurs applications / Kalman recursion generalizations and their applications Kadhim, Sadeq 20 April 2018 (has links) Nous considérions des modèles à espace d'état où les observations sont multicatégorielles et longitudinales, et l'état est décrit par des modèles du type CHARN. Nous estimons l'état au moyen des récursivités de Kalman généralisées. Celles-ci reposent sur l'application d'une variété de filtres particulaires et de l’algorithme EM. Nos résultats sont appliqués à l'estimation du trait latent en qualité de vie. Ce qui fournit une alternative et une généralisation des méthodes existantes dans la littérature. Ces résultats sont illustrés par des simulations numériques et une application aux données réelles sur la qualité de vie des femmes ayant subi une opération pour cause de cancer du sein / We consider state space models where the observations are multicategorical and longitudinal, and the state is described by CHARN models. We estimate the state by generalized Kalman recursions, which rely on a variety of particle filters and EM algorithm. Our results are applied to estimating the latent trait in quality of life, and this furnishes an alternative and a generalization of existing methods. These results are illustrated by numerical simulations and an application to real data in the quality of life of patients surged for breast cancer Récursivités de Kalman généralisées Variables latentes Filtres particulaires Algorithme EM Generalized Kalman recursions Generalized state space models Multicategorical longitudinal data Latent variables Particle filters EM algorithm 519.5 511.3 004.015 1
2	Representation and Reconstruction of Linear, Time-Invariant Networks Woodbury, Nathan Scott 01 April 2019 (has links) Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required) and not necessarily strictly proper.The ultimate objective of this dissertation is to develop algorithms capable of reconstructing proper networks, and this objective will be addressed in three parts. The first part lays the foundation for the theory of mathematical representations of proper networks, including an exposition on when such networks are well-posed (i.e., physically realizable). The second part studies the notions of abstractions of a network, which are other networks that preserve certain properties of the original network but contain less structural information. As such, abstractions require less a priori information to reconstruct from data than the original network, which allows previously-unsolvable problems to become solvable. The third part addresses our original objective and presents reconstruction algorithms to recover proper networks in both the time domain and in the frequency domain. Dynamic systems networks network reconstruction target specificity system identification learning linear systems system representations state space models generalized state space models dynamical structure functions dynamical network functions DSF DNF multi-DSF feedback well-posedness algebraic loops representability abstraction immersion identifiability informativity data proper systems strictly proper systems causality strict causality frequency domain time domain Computer Sciences Physical Sciences and Mathematics

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Les généralisations des récursivités de Kalman et leurs applications / Kalman recursion generalizations and their applications

Representation and Reconstruction of Linear, Time-Invariant Networks