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The far-reaching successes of deep neural networks in a wide variety of learning tasks have prompted research on how model properties account for high network performance. For a specific class of models whose activation functions are piecewise linear, one such property of interest is the number of linear regions that the network generates. Such models themselves define piecewise linear functions by partitioning input space into disjoint regions and fitting a different linear function on each such piece. It would be expected that the number or configuration of such regions would describe the model’s ability to fit complicated functions. However, previous works have shown difficulty in identifying linear regions as satisfactory predictors of model success. In this thesis, the question of whether the generation of linear regions due to training encode the properties of the learning problem is explored. More specifically, it is investigated whether change in linear region density due to model fitting is related to the geometric properties of the training data. In this work, data geometry is characterized in terms of the curvature of the underlying manifold. Models with ReLU activation functions are trained on a variety of regression problems defined on artificial manifolds and the change in linear region density is recorded along trajectories in input space. Learning is performed on problems defined on curves, surfaces and for image data. Experiments are repeated as the data geometry is varied and the change in density is compared with the manifold curvature measure used. In no experimental setting, was the observed change in density found to be clearly linked with curvature. However, density was observed to increase at points of discontinuity. This suggests that linear regions can in some instances model data complexities, however, the findings presented here do not support that data curvature is encoded by the formation of linear regions. Thus, the role that linear regions play in controlling the capacity of piecewise linear networks remains open. Future research is needed to gain further insights into how data geometry and linear regions are connected. / De breda framgångar som djupa neurala nätverk har uppvisat i en mängd olika inlärningsproblem har inspirerat ny forskning med syfte att förklara vilka modellegenskaper som resulterar i högpresterande nätverk. För neurala nätverk som använder styckvis linjära aktiveringsfunktioner är en intressant egenskap att studera de linjära regioner som nätverket genererar i det vektorrum som utgör träningsdatans definitionsmängd. Nätverk med styckvis linjära aktiveringsfunktioner delar upp definitionsmängden i distinkta regioner på vilka olika linjära funktioner avbildas. Dessa nätverk avbildar själva styckvis linjära funktioner. Genom att anpassa flera skilda linjära avbildningar går det att approximera funktioner som är icke-linjära. Därför skulle man kunna förvänta sig att antalet linjära regioner som en modell genererar och hur de är fördelade i rummet kunde fungera som mått på modellens förmåga att lära sig komplicerade funktioner. Tidigare efterforskingar inom detta område har dock inte kunnat demonstrera ett samband mellan antalet eller fördelningen av linjära regioner och modellens prestanda. I den här avhandlingen undersöks det vilken roll linjära regioner spelar i att förklara en modells kapacitet och vad den lär sig. Fångar de linjära regioner som ett nätverk lär sig de underliggande egenskaperna hos träningsdatan? Mer specifikt så studeras huruvida den lokala förändringen i antalet linjära regioner efter modellträning korrelerar med träningsdatans geometri. Träningsdata genereras från syntetiska mångfalder och datageometrin beskrivs i termer av mångfaldens krökning. På dessa mångfalder definieras regressionsproblem och träning upprepas för topologier av olika form och med olika krökning. Skillnaden i antalet linjära regioner efter träning mäts längs banor i definitionsdomänen och jämförs med datans krökning. Ingen av de experiment som utfördes lyckades påvisa något tydligt samband mellan förändring i antal regioner och datans krökning. Det observerades dock att antalet linjära regioner ökar i närheten av punkter som utgör diskontinuiteter. Detta antyder att linjära regioner under vissa omständigheter kan modellera komplexitet. Således förblir rollen som linjära regioner har i att förklara modellförmåga diffus.

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This paper examines the extent to which thin markets in conjunction with tax induced kinks in the budget constraint cause consumer demand to be skewed. To illustrate the principles I focus on the demand for owner-occupied housing. Housing units are indivisible and heterogeneous while tastes for housing are at least partly idiosyncratic, causing housing markets to be thin. In addition, prior to 1998, capital gains tax provisions introduced a sharp kink in the budget constraint of existing owner-occupiers in search of a new home: previous homeowners under age 55 paid no capital gains tax if they bought up, but were subject to capital gains tax if they bought down. I first characterize the economic conditions under which households err on the up or down side when choosing a home in the presence of a thin market and a kinked budget constraint. I then specify an empirical model that takes such effects into account. Results based on Monte Carlo experiments indicate that failing to allow for skewness in the demand for housing leads to biased estimates of the elasticities of demand when such skewness is actually present. In addition, estimates based on American Housing Survey data suggest that such bias is substantial: controlling for skewness reduces the price elasticity of demand among previous owner-occupiers from 1.6 to 0.3. Moreover, 58% of previous homeowners err on the up while only 42% err on the down side. Thus, housing demand is skewed. / Ph. D.

O presente trabalho apresenta um método de projeto de controlador com alocação de pólos em sistemas fuzzy utilizando funções de Lyapunov por partes e contínuas no espaço de estado. A idéia principal é utilizar controladores chaveados no espaço de estado para obter uma resposta transitória satisfatória do sistema, obtida pela localização dos pólos. A modelagem fuzzy Takagi-Sugeno é utilizada para representar um sistema não-linear em diversos pontos de linearização através de uma aproximação por vários modelos locais lineares invariantes no tempo. A análise de estabilidade e o projeto de sistemas de controle podem se formulados em termos de desigualdades matriciais lineares (em inglês, linear matrix inequalities (LMIs)), as quais são resolvidas por técnicas de programação convexa. Na análise de estabilidade ou na síntese de um controlador em sistemas fuzzy é necessário resolver um número determinado de LMIs de acordo com o número de modelos locais. Encontrar uma função de Lyapunov comum a todos os modelos locais pode ser inviável, especialmente quando se impõem critérios de desempenho, que aparecem como restrições no contexto de LMIs. A proposta de uma função de Lyapunov por partes objetiva diminuir o conservadorismo na busca de um controlador que leve os pólos de malha fechada à uma região desejada. Resultados de análise e síntese da teoria de sistemas lineares por partes contribuíram para a construção do resultado apresentado. Exemplos com simulação ilustram o método proposto. / This work presents a controller design method for fuzzy dynamic systems based on piecewise Lyapunov functions with constraints on the closed-loop pole location. The main idea is to use switched controllers to locate the poles of the system to obtain a satisfactory transient response. The pole placement strategy allows to specify the performance in terms of the desired time response of the feedback system. The Takagi-Sugeno fuzzy model can approximate the nonlinear system in several linearization points using linear time invariant systems. Thus, a global fuzzy model can be obtained from a fuzzy combination of these linear systems. Stability analysis and design of fuzzy control systems can be efficiently carried out in the context of linear matrix inequalities (LMIs). If the fuzzy system is described by many local models, the resulting set of LMIs may be infeasible. The search for a Lyapunov function in the fuzzy pole placement problem may be easier to be satisfied in a piecewise framework. Some results from piecewise linear systems theory have contributed to the development of the presented technique. Some examples are given to illustrate the proposed method.

Actuellement la nécessité des systèmes d’alimentation d’énergie, capables d’assurer un fonctionnement stable dans des domaines de fonctionnement assez larges avec des bonnes performances dynamiques (rapidité du système, variations limitées de la tension de sortie en réponse aux perturbations de charge ou de tension d’alimentation), devient de plus en plus importante. De ce fait, cette thèse est orientée sur la commande des convertisseurs de puissance DC-DC représentés par des modèles hybrides.En tenant compte de la structure variable de ces systèmes à commutation, un modèle hybride permet de décrire plus précisément le comportement dynamique d’un convertisseur dans son domaine de fonctionnement. Dans cette optique, l’approximation PWA est utilisée afin de modéliser les convertisseurs DC-DC. A partir des modèles hybrides développés, on s’est intéressé à la stabilisation des convertisseurs au moyen des correcteurs à gains commutés élaborés sur la base de fonctions de Lyapunov PWQ, et à l’implantation d’une commande prédictive explicite, en considérant des contraintes sur l’entrée de commande. La méthode de modélisation et les stratégies de commande proposées ont été appliquées sur deux topologies : un convertisseur buck, afin de mieux maîtriser le réglage des correcteurs et un convertisseur flyback avec filtre d’entrée. Cette dernière topologie nous a permis de répondre aux difficultés du point de vue de la commande (comportement à déphasage non-minimal) rencontrées dans la majorité des convertisseurs DC-DC. Les performances des commandes élaborées ont été validées en simulation sur les topologies considérées et expérimentalement sur une maquette du convertisseur buck. / Lately, power supply systems, guaranteeing the global stability for large enough operation ranges with good dynamic performances (small settling time, bounded overshoot of the output voltage in the presence of load or supply voltage variations), are strongly needed. Therefore, this thesis deals with control problems of DC-DC power converters represented by hybrid models.Considering the variable structure of these switched systems, a hybrid model describes more precisely the converter’s dynamics in its operating domain. From this perspective, a PWA (piecewise affine) approximation is used in order to model the DC-DC converters. Based on the developed hybrid models, first we have designed a stable piecewise linear state-feedback controller using piecewise quadratic (PWQ) Lyapunov functions, and secondly, we have implemented an explicit predictive control law taking into account constraints on the control input. The hybrid modeling technique and the proposed control strategies were applied on two different topologies of converters: a buck converter, in order to have a thorough knowledge of the controllers’ tuning, and a flyback converter with an input filter. This last topology, allowed us to manage different control problems (non-minimum phase behavior) encountered in the majority of topologies of DC-DC power converters. The controllers’ performances were validated in simulation on both considered topologies and also experimentally on buck converter.

Neste trabalho é apresentada uma ferramenta de otimização para análise de problemas de alocação de água em bacias hidrográficas utilizando técnicas de programação linear e linear por partes, integradas a um modelo de amortecimentos de ondas em canais. A otimização é feita de forma global, com uso de softwares de programação linear baseados nos métodos de pontos interiores. A metodologia de uso do sistema consiste em se obter uma solução ?ótima? para situações de disponibilidade de água insuficiente a todos os usos conflitantes na bacia. A ferramenta está sendo acoplada e incorporada ao AcquaNet, um Sistema de Suporte a Decisões (SSD) para análise de sistemas de recursos hídricos, que utiliza um algoritmo de rede de fluxo afim de otimizar a alocação de água. A formulação utilizando programação linear permite a análise global do sistema e por isso, espera-se melhor aproveitamento da água disponível, seja no menor déficit de atendimento às demandas ou maior armazenamento nos reservatórios. A programação linear com utilização de métodos de pontos interiores é atualmente uma técnica bastante conhecida e bem desenvolvida. Existem vários pacotes computacionais gratuitos com implementações eficientes dos métodos de pontos interiores que motivaram sua utilização neste trabalho. / This work presents an optimization tool for analyzing the problems of water allocation in watersheds by utilizing techniques of linear and piecewise linear programming integrated to a pattern of stream flow routing. The optimization is done in a global way with the usage of linear programming packages based upon the Internal Point Methods. The methodology of the usage consists in the acquirement of an optimal solution for situation of insufficient water availability for all conflicting consumptions from the watershed. The tool is being attached and incorporated to AcquaNet, which is a decision support system (DSS) for analysis of water resources systems that utilizes a network flow algorithm, with the purpose of optimizing the water allocation. The formulation that uses the linear programming leads to the analysis of the system as a whole and for this reason it is expected a better usage of the available water with a lower deficit in the supply or a greater storage in the reservoirs. Linear Programming with Internal Point Methods is nowadays a well known and very well developed technique. There are several computational packages with efficient implementations of the Internal Points Methods freely available, and that, has brought great motivation in its usage in the present work.

O presente trabalho apresenta um método de projeto de controlador com alocação de pólos em sistemas fuzzy utilizando funções de Lyapunov por partes e contínuas no espaço de estado. A idéia principal é utilizar controladores chaveados no espaço de estado para obter uma resposta transitória satisfatória do sistema, obtida pela localização dos pólos. A modelagem fuzzy Takagi-Sugeno é utilizada para representar um sistema não-linear em diversos pontos de linearização através de uma aproximação por vários modelos locais lineares invariantes no tempo. A análise de estabilidade e o projeto de sistemas de controle podem se formulados em termos de desigualdades matriciais lineares (em inglês, linear matrix inequalities (LMIs)), as quais são resolvidas por técnicas de programação convexa. Na análise de estabilidade ou na síntese de um controlador em sistemas fuzzy é necessário resolver um número determinado de LMIs de acordo com o número de modelos locais. Encontrar uma função de Lyapunov comum a todos os modelos locais pode ser inviável, especialmente quando se impõem critérios de desempenho, que aparecem como restrições no contexto de LMIs. A proposta de uma função de Lyapunov por partes objetiva diminuir o conservadorismo na busca de um controlador que leve os pólos de malha fechada à uma região desejada. Resultados de análise e síntese da teoria de sistemas lineares por partes contribuíram para a construção do resultado apresentado. Exemplos com simulação ilustram o método proposto. / This work presents a controller design method for fuzzy dynamic systems based on piecewise Lyapunov functions with constraints on the closed-loop pole location. The main idea is to use switched controllers to locate the poles of the system to obtain a satisfactory transient response. The pole placement strategy allows to specify the performance in terms of the desired time response of the feedback system. The Takagi-Sugeno fuzzy model can approximate the nonlinear system in several linearization points using linear time invariant systems. Thus, a global fuzzy model can be obtained from a fuzzy combination of these linear systems. Stability analysis and design of fuzzy control systems can be efficiently carried out in the context of linear matrix inequalities (LMIs). If the fuzzy system is described by many local models, the resulting set of LMIs may be infeasible. The search for a Lyapunov function in the fuzzy pole placement problem may be easier to be satisfied in a piecewise framework. Some results from piecewise linear systems theory have contributed to the development of the presented technique. Some examples are given to illustrate the proposed method.