This thesis explores the use of artificial neural networks for modelling cognitive processes. It presents an exposition of the neural network paradigm, and evaluates its viability in relation to the classical, symbolic approach in cognitive science. Classical researchers have approached the description of cognition by concentrating mainly on an abstract, algorithmic level of description in which the information processing properties of cognitive processes are emphasised. The approach is founded on seminal ideas about computation, and about algorithmic description emanating, amongst others, from the work of Alan Turing in mathematical logic. In contrast to the classical conception of cognition, neural network approaches are based on a form of neurocomputation in which the parallel distributed processing mechanisms of the brain are highlighted. Although neural networks are generally accepted to be more neurally plausible than their classical counterparts, some classical researchers have argued that these networks are best viewed as implementation models, and that they are therefore not of much relevance to cognitive researchers because information processing models of cognition can be developed independently of considerations about implementation in physical systems. In the thesis I argue that the descriptions of cognitive phenomena deriving from neural network modelling cannot simply be reduced to classical, symbolic theories. The distributed representational mechanisms underlying some neural network models have interesting properties such as similarity-based representation, content-based retrieval, and coarse coding which do not have straightforward equivalents in classical systems. Moreover, by placing emphasis on how cognitive processes are carried out by brain-like mechanisms, neural network research has not only yielded a new metaphor for conceptualising cognition, but also a new methodology for studying cognitive phenomena. Neural network simulations can be lesioned to study the effect of such damage on the behaviour of the system, and these systems can be used to study the adaptive mechanisms underlying learning processes. For these reasons, neural network modelling is best viewed as a significant theoretical orientation in the cognitive sciences, instead of just an implementational endeavour. / Psychology / D. Litt. et Phil. (Psychology)

La mousse de polyuréthane est un matériau cellulaire caractérisé par un spectre de propriétés mécaniques intéressant : une faible densité, une capacité à absorber l’énergie de déformation et une faible raideur.Elle présente également des propriétés telles qu’une excellente isolation thermique et acoustique, une forte absorption des liquides et une diffusion complexe de la lumière. Ce spectre de propriétés fait de la mousse de polyuréthane un des matériaux couramment utilisés dans de nombreuses applications phoniques, thermiques et de confort. Pour contrôler la vibration transmise aux occupants des sièges, plusieurs dispositifs automatiques de régulation et de contrôle sont actuellement en cours de développement tels que les amortisseurs actifs et semi-actifs. La performance de ces derniers dépend bien évidemment de la prédiction des comportements de tous les composants du siège et en particulier la mousse. D’une façon générale, il est indispensable de modéliser le comportement mécanique complexe de la mousse de polyuréthane et d’identifier ses propriétés quasi-statique et dynamiques afin d’optimiser la conception des systèmes incluant la mousse en particulier l’optimisation de l’aspect confort. Dans cette optique, l’objectif principal de cette thèse consiste à implémenter des modèles mécaniques de la mousse de polyuréthane fiables et capables de prévoir sa réponse sous différentes conditions d’essais. Dans la littérature, on retrouve les divers modèles développés tels que les modèles de mémoire entier et fractionnaire. L’inconvénient majeur de ces modèles est lié à la dépendance de leurs paramètres vis-à-vis des conditions d’essais, chose qui affecte le caractère général de leur représentativité des comportements quasi-statique et dynamique de la mousse polyuréthane. Pour pallier à cet inconvénient, nous avons développé des modèles qui, grâce à des choix judicieux de méthodes d’identification, assurent une représentativité plus générale des comportements quasi-statique et dynamique de la mousse polyuréthane. En effet, nous avons démontré qu’on peut exprimer les paramètres dimensionnels des modèles développés par le produit de deux parties indépendantes ; une regroupant les conditions d’essais et une autre définissant les paramètres adimensionnels et invariants qui caractérisent le matériau. Ces résultats ont été obtenus à partir de plusieurs études expérimentales qui ont permis l’appréhension du comportement quasi-statique (à travers des essais de compression unidirectionnelle) et dynamique (à travers des tests en vibration entretenue). La mousse, sous des grandes déformations, présente à la fois un comportement élastique non linéaire et un comportement viscoélastique. En outre, une discrimination entre les modèles développés particulièrement en quasi-statique a été effectuée. Les avantages et les limites de chacun y ont été discutés. / Polyurethane foam is a cellular material characterized by an interesting mechanical spectrum of properties: low density, capacity to absorb the deformation energy and low stiffness. It presents also several other properties, such as excellent thermal and acoustic insulation, high absorption of fluids and a complex scattering of light. This spectrum of properties makes polyurethane foam commonly used in many thermal, acoustic and comfort applications. To control the vibration transmitted to the seat occupants, several automatic devices for regulation and control are currently outstanding developments like active and semi-active dampers. The performance of these devices depends, of course, on the prediction of the behaviour of all the seat components and especially foam. Generally, it is essential to model the complex mechanical behaviour of polyurethane foam and identify its quasi-staticand dynamicproperties in order to optimize the design of systems with foam particularly the optimization of the comfort aspect. In this mind, the main goal of this thesis is to implement mechanical models of polyurethane foam reliable and able to provide its response under different test conditions. Several models has been developed in literature such as memory fractional and integer models. The main disadvantage of these models is the dependence of their parameters against the test conditions. It affects the general character of their representativeness to the quasi-static and dynamic behaviours of polyurethane foam. To solve this problem, we developed models with specific identification methods to ensure broader representation of the quasi-static and dynamic behaviour of polyurethane foam. Indeed, we have demonstrated that we can express the dimensional parameters of the developed models by the product of two independent parts; the first contain only the test conditions and the second define the dimensionless and invariant parameters that characterize the foam material. The developed models have been establish after several experimental studies allowing the apprehension of the quasi-static behaviour (through unidirectional compression tests) and the dynamic behaviour (through harmonic vibration tests). The polyurethane foam, under large deformations, exhibits a non linear elastic behaviour and viscoelastic behaviour. In addition, discrimination between the models developed especially in quasi-static case has been conducted. The advantages and limitations of each model have been discussed.

Made available in DSpace on 2014-12-17T15:47:50Z (GMT). No. of bitstreams: 1 BrunoCO.pdf: 708202 bytes, checksum: 3eb4368a0c268064bcd6ad892e1f2c0c (MD5) Previous issue date: 2009-05-22 / The constant increase of complexity in computer applications demands the development of more powerful hardware support for them. With processor's operational frequency reaching its limit, the most viable solution is the use of parallelism. Based on parallelism techniques and the progressive growth in the capacity of transistors integration in a single chip is the concept of MPSoCs (Multi-Processor System-on-Chip). MPSoCs will eventually become a cheaper and faster alternative to supercomputers and clusters, and applications developed for these high performance systems will migrate to computers equipped with MP-SoCs containing dozens to hundreds of computation cores. In particular, applications in the area of oil and natural gas exploration are also characterized by the high processing capacity required and would benefit greatly from these high performance systems. This work intends to evaluate a traditional and complex application of the oil and gas industry known as reservoir simulation, developing a solution with integrated computational systems in a single chip, with hundreds of functional unities. For this, as the STORM (MPSoC Directory-Based Platform) platform already has a shared memory model, a new distributed memory model were developed. Also a message passing library has been developed folowing MPI standard / O constante aumento da complexidade das aplica??es demanda um suporte de hardware computacionalmente mais poderoso. Com a aproxima??o do limite de velocidade dos processadores, a solu??o mais vi?vel ? o paralelismo. Baseado nisso e na crescente capacidade de integra??o de transistores em um ?nico chip surgiram os chamados MPSoCs (Multiprocessor System-on-Chip) que dever?o ser, em um futuro pr?ximo, uma alternativa mais r?pida e mais barata aos supercomputadores e clusters. Aplica??es tidas como destinadas exclusivamente a execu??o nesses sistemas de alto desempenho dever?o migrar para m?quinas equipadas com MPSoCs dotados de dezenas a centenas de n?cleos computacionais. Aplica??es na ?rea de explora??o de petr?leo e g?s natural tamb?m se caracterizam pela enorme capacidade de processamento requerida e dever?o se beneficiar desses novos sistemas de alto desempenho. Esse trabalho apresenta uma avalia??o de uma tradicional e complexa aplica??o da ind?stria de petr?leo e g?s natural, a simula??o de reservat?rios, sob a nova ?tica do desenvolvimento de sistemas computacionais integrados em um ?nico chip, dotados de dezenas a centenas de unidades funcionais. Para isso, um modelo de mem?ria distribu?da foi desenvolvido para a plataforma STORM (MPSoC Directory-Based Platform), que j? contava com um modelo de mem?ria compartilhada. Foi desenvolvida, ainda, uma biblioteca de troca de mensagens para esse modelo de mem?ria seguindo o padr?o MPI

This thesis explores the use of artificial neural networks for modelling cognitive processes. It presents an exposition of the neural network paradigm, and evaluates its viability in relation to the classical, symbolic approach in cognitive science. Classical researchers have approached the description of cognition by concentrating mainly on an abstract, algorithmic level of description in which the information processing properties of cognitive processes are emphasised. The approach is founded on seminal ideas about computation, and about algorithmic description emanating, amongst others, from the work of Alan Turing in mathematical logic. In contrast to the classical conception of cognition, neural network approaches are based on a form of neurocomputation in which the parallel distributed processing mechanisms of the brain are highlighted. Although neural networks are generally accepted to be more neurally plausible than their classical counterparts, some classical researchers have argued that these networks are best viewed as implementation models, and that they are therefore not of much relevance to cognitive researchers because information processing models of cognition can be developed independently of considerations about implementation in physical systems. In the thesis I argue that the descriptions of cognitive phenomena deriving from neural network modelling cannot simply be reduced to classical, symbolic theories. The distributed representational mechanisms underlying some neural network models have interesting properties such as similarity-based representation, content-based retrieval, and coarse coding which do not have straightforward equivalents in classical systems. Moreover, by placing emphasis on how cognitive processes are carried out by brain-like mechanisms, neural network research has not only yielded a new metaphor for conceptualising cognition, but also a new methodology for studying cognitive phenomena. Neural network simulations can be lesioned to study the effect of such damage on the behaviour of the system, and these systems can be used to study the adaptive mechanisms underlying learning processes. For these reasons, neural network modelling is best viewed as a significant theoretical orientation in the cognitive sciences, instead of just an implementational endeavour. / Psychology / D. Litt. et Phil. (Psychology)