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Estudo da dinâmica de evolução do HIV em seres humanos utilizando sistema de equações diferenciais ordináriasVicentin, Daniel Chieregato January 2019 (has links)
Orientador: Tiago de Carvalho / Resumo: O objetivo desta dissertação é abordar aspectos qualitativos de sistemas de equações diferenciais ordinárias e sistemas contínuos suaves por partes aplicados à dinâmica do Vírus da Imunodeficiência Humana (HIV). Neste trabalho, apresentamos um modelo matemático que descreve a dinâmica do HIV no corpo humano e o analisamos através da matriz da próxima geração e teoria de estabilidade, com a finalidade de prever se a doença fica ou não controlada. Posteriormente, estudamos um sistema de equações diferenciais ordinárias usado para modelar a dinâmica do vírus para diferentes tipos de tratamentos. Tal modelo foi explorado qualitativamente de duas maneiras: por um sistema contínuo (pelo método de Korobeinikov) e por um descontínuo (pelas convenções de Filippov). Analisamos o comportamento dinâmico de terapias antirretrovirais, visando a diminuição das concentrações virais no sangue, de acordo com a análise da estabilidade realizada. / Abstract: The goal of this dissertation is to study qualitative aspects about systems of ordinary differential equations and piecewise smooth systems applied to the dynamic of Human Immunodeficiency Virus (HIV). In this work, we present a mathematical model that describes the dynamic of HIV in the human body and we analyze this model by next-generation matrix and stability theory in order to predict if the disease becomes stable, and thus stop virus transmission. In addition, we studied another system of ordinary differential equations that were proposed to model the HIV dynamics assuming different therapies. We have explored qualitatively the model by two distinct approaches: a continuous system (by Korobeinikov method) and a discontinuous system (by Filippov theory). Due to the stability analysis, it was possible to understand the dynamics of anti-retroviral therapies, which are responsible for decreasing the concentration of detectable HIV in blood. / Mestre
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Identification de systèmes dynamiques hybrides : géométrie, parcimonie et non-linéarités / Hybrid dynamical system identification : geometry, sparsity and nonlinearitiesLe, Van Luong 04 October 2013 (has links)
En automatique, l'obtention d'un modèle du système est la pierre angulaire des procédures comme la synthèse d'une commande, la détection des défaillances, la prédiction... Cette thèse traite de l'identification d'une classe de systèmes complexes, les systèmes dynamiques hybrides. Ces systèmes impliquent l'interaction de comportements continus et discrets. Le but est de construire un modèle à partir de mesures expérimentales d'entrée et de sortie. Une nouvelle approche pour l'identification de systèmes hybrides linéaires basée sur les propriétés géométriques des systèmes hybrides dans l'espace des paramètres est proposée. Un nouvel algorithme est ensuite proposé pour le calcul de la solution la plus parcimonieuse (ou creuse) de systèmes d'équations linéaires sous-déterminés. Celui-ci permet d'améliorer une approche d'identification basée sur l'optimisation de la parcimonie du vecteur d'erreur. De plus, de nouvelles approches, basées sur des modèles à noyaux, sont proposées pour l'identification de systèmes hybrides non linéaires et de systèmes lisses par morceaux / In automatic control, obtaining a model is always the cornerstone of the synthesis procedures such as controller design, fault detection or prediction... This thesis deals with the identification of a class of complex systems, hybrid dynamical systems. These systems involve the interaction of continuous and discrete behaviors. The goal is to build a model from experimental measurements of the system inputs and outputs. A new approach for the identification of linear hybrid systems based on the geometric properties of hybrid systems in the parameter space is proposed. A new algorithm is then proposed to recover the sparsest solutions of underdetermined systems of linear equations. This allows us to improve an identification approach based on the error sparsification. In addition, new approaches based on kernel models are proposed for the identification of nonlinear hybrid systems and piecewise smooth systems
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