Дисертації з теми "Réseaux de neurones binaires"
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Mayorquim, Jorge Luiz. "Étude en vue de la réalisation d'un réseau de neurones binaires logiques : détection de contours en temps réel." Compiègne, 1996. http://www.theses.fr/1996COMPD893.
Повний текст джерелаWenzek, Didier. "Construction de réseaux de neurones." Phd thesis, Grenoble INPG, 1993. http://tel.archives-ouvertes.fr/tel-00343569.
Повний текст джерелаTsopze, Norbert. "Treillis de Galois et réseaux de neurones : une approche constructive d'architecture des réseaux de neurones." Thesis, Artois, 2010. http://www.theses.fr/2010ARTO0407/document.
Повний текст джерелаThe artificial neural networks are successfully applied in many applications. But theusers are confronted with two problems : defining the architecture of the neural network able tosolve their problems and interpreting the network result. Many research works propose some solutionsabout these problems : to find out the architecture of the network, some authors proposeto use the problem domain theory and deduct the network architecture and some others proposeto dynamically add neurons in the existing networks until satisfaction. For the interpretabilityproblem, solutions consist to extract rules which describe the network behaviour after training.The contributions of this thesis concern these problems. The thesis are limited to the use of theartificial neural networks in solving the classification problem.In this thesis, we present a state of art of the existing methods of finding the neural networkarchitecture : we present a theoritical and experimental study of these methods. From this study,we observe some limits : difficulty to use some method when the knowledges are not available ;and the network is seem as ’black box’ when using other methods. We a new method calledCLANN (Concept Lattice-based Artificial Neural Network) which builds from the training dataa semi concepts lattice and translates this semi lattice into the network architecture. As CLANNis limited to the two classes problems, we propose MCLANN which extends CLANN to manyclasses problems.A new method of rules extraction called ’MaxSubsets Approach’ is also presented in thisthesis. Its particularity is the possibility of extracting the two kind of rules (If then and M-of-N)from an internal structure.We describe how to explain the MCLANN built network result aboutsome inputs
Voegtlin, Thomas. "Réseaux de neurones et auto-référence." Lyon 2, 2002. http://theses.univ-lyon2.fr/documents/lyon2/2002/voegtlin_t.
Повний текст джерелаThe purpose of this thesis is to present a class of unsupervised learning algorithms for recurrent networks. In the first part (chapters 1 to 4), I propose a new approach to this question, based on a simple principle: self-reference. A self-referent algorithm is not based on the minimization of an objective criterion, such as an error function, but on a subjective function, that depends on what the network has previously learned. An example of a supervised recurrent network where learning is self-referent is the Simple Recurrent Network (SRN) by Elman (1990). In the SRN, self-reference is applied to the supervised error back-propagation algorithm. In this aspect, the SRN differs from other generalizations of back-propagation to recurrent networks, that use an objective criterion, such as Back-Propagation Through Time, or Real-Time Recurrent Learning. In this thesis, I show that self-reference can be combined with several well-known unsupervised learning methods: the Self-Organizing Map (SOM), Principal Components Analysis (PCA), and Independent Components Analysis (ICA). These techniques are classically used to represent static data. Self-reference allows one to generalize these techniques to time series, and to define unsupervised learning algorithms for recurrent networks
Teytaud, Olivier. "Apprentissage, réseaux de neurones et applications." Lyon 2, 2001. http://theses.univ-lyon2.fr/documents/lyon2/2001/teytaud_o.
Повний текст джерелаCôté, Marc-Alexandre. "Réseaux de neurones génératifs avec structure." Thèse, Université de Sherbrooke, 2017. http://hdl.handle.net/11143/10489.
Повний текст джерелаJodouin, Jean-François. "Réseaux de neurones et traitement du langage naturel : étude des réseaux de neurones récurrents et de leurs représentations." Paris 11, 1993. http://www.theses.fr/1993PA112079.
Повний текст джерелаBrette, Romain. "Modèles Impulsionnels de Réseaux de Neurones Biologiques." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2003. http://tel.archives-ouvertes.fr/tel-00005340.
Повний текст джерелаTardif, Patrice. "Autostructuration des réseaux de neurones avec retards." Thesis, Université Laval, 2007. http://www.theses.ulaval.ca/2007/24240/24240.pdf.
Повний текст джерелаMaktoobi, Sheler. "Couplage diffractif pour réseaux de neurones optiques." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCD019.
Повний текст джерелаPhotonic networks with high performance can be considered as substrates for future computing systems. In comparison with electronics, photonic systems have substantial privileges, for instance the possibility of a fully parallel implementation of networks. Recently, neural networks have moved into the center of attention of the photonic community. One of the most important requirements for parallel large-scale photonic networks is to realize the connectivities. Diffraction is considered as a method to process the connections between the nodes (coupling) in optical neural networks. In the current thesis, we evaluate the scalability of a diffractive coupling in more details as follow:First, we begin with a general introductions for artificial intelligence, machine learning, artificial neural network and photonic neural networks. To establish a working neural network, learning rules are an essential part to optimize a configuration for obtaining a low error from the system, hence learning rules are introduced (Chapter 1). We investigate the fundamental concepts of diffractive coupling in our spatio-temporal reservoir. In that case, theory of diffraction is explained. We use an analytical scheme to provide the limits for the size of diffractive networks which is a part of our photonic neural network (Chapter 2). The concepts of diffractive coupling are investigated experimentally by two different experiments to confirm the analytical limits and to obtain maximum number of nodes which can be coupled in the photonic network (Chapter 3). Numerical simulations for such an experimental setup is modeled in two different schemes to obtain the maximum size of network numerically, which approaches a surface of 100 mm2 (Chapter 4). Finally, the complete photonic neural network is demonstrated. We design a spatially extended reservoir for 900 nodes. Consequently, our system generalizes the prediction for the chaotic Mackey–Glass sequence (Chapter 5)
Ouali, Jamel. "Architecture intégrée flexible pour réseaux de neurones." Grenoble INPG, 1991. http://www.theses.fr/1991INPG0035.
Повний текст джерелаBigot, Pascal. "Utilisation des réseaux de neurones pour la télégestion des réseaux techniques urbains." Lyon 1, 1995. http://www.theses.fr/1995LYO10036.
Повний текст джерелаKoiran, Pascal. "Puissance de calcul des réseaux de neurones artificiels." Lyon 1, 1993. http://www.theses.fr/1993LYO19003.
Повний текст джерелаGraïne, Slimane. "Inférence grammaticale régulière par les réseaux de neurones." Paris 13, 1994. http://www.theses.fr/1994PA132020.
Повний текст джерелаLe, Fablec Yann. "Prévision de trajectoires d'avions par réseaux de neurones." Toulouse, INPT, 1999. http://www.theses.fr/1999INPT034H.
Повний текст джерелаCorne, Christophe. "Parallélisation de réseaux de neurones sur architecture distribuée." Mulhouse, 1999. http://www.theses.fr/1999MULH0583.
Повний текст джерелаFernandez, Brillet Lucas. "Réseaux de neurones CNN pour la vision embarquée." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM043.
Повний текст джерелаRecently, Convolutional Neural Networks have become the state-of-the-art soluion(SOA) to most computer vision problems. In order to achieve high accuracy rates, CNNs require a high parameter count, as well as a high number of operations. This greatly complicates the deployment of such solutions in embedded systems, which strive to reduce memory size. Indeed, while most embedded systems are typically in the range of a few KBytes of memory, CNN models from the SOA usually account for multiple MBytes, or even GBytes in model size. Throughout this thesis, multiple novel ideas allowing to ease this issue are proposed. This requires to jointly design the solution across three main axes: Application, Algorithm and Hardware.In this manuscript, the main levers allowing to tailor computational complexity of a generic CNN-based object detector are identified and studied. Since object detection requires scanning every possible location and scale across an image through a fixed-input CNN classifier, the number of operations quickly grows for high-resolution images. In order to perform object detection in an efficient way, the detection process is divided into two stages. The first stage involves a region proposal network which allows to trade-off recall for the number of operations required to perform the search, as well as the number of regions passed on to the next stage. Techniques such as bounding box regression also greatly help reduce the dimension of the search space. This in turn simplifies the second stage, since it allows to reduce the task’s complexity to the set of possible proposals. Therefore, parameter counts can greatly be reduced.Furthermore, CNNs also exhibit properties that confirm their over-dimensionment. This over-dimensionement is one of the key success factors of CNNs in practice, since it eases the optimization process by allowing a large set of equivalent solutions. However, this also greatly increases computational complexity, and therefore complicates deploying the inference stage of these algorithms on embedded systems. In order to ease this problem, we propose a CNN compression method which is based on Principal Component Analysis (PCA). PCA allows to find, for each layer of the network independently, a new representation of the set of learned filters by expressing them in a more appropriate PCA basis. This PCA basis is hierarchical, meaning that basis terms are ordered by importance, and by removing the least important basis terms, it is possible to optimally trade-off approximation error for parameter count. Through this method, it is possible to compress, for example, a ResNet-32 network by a factor of ×2 both in the number of parameters and operations with a loss of accuracy <2%. It is also shown that the proposed method is compatible with other SOA methods which exploit other CNN properties in order to reduce computational complexity, mainly pruning, winograd and quantization. Through this method, we have been able to reduce the size of a ResNet-110 from 6.88Mbytes to 370kbytes, i.e. a x19 memory gain with a 3.9 % accuracy loss.All this knowledge, is applied in order to achieve an efficient CNN-based solution for a consumer face detection scenario. The proposed solution consists of just 29.3kBytes model size. This is x65 smaller than other SOA CNN face detectors, while providing equal detection performance and lower number of operations. Our face detector is also compared to a more traditional Viola-Jones face detector, exhibiting approximately an order of magnitude faster computation, as well as the ability to scale to higher detection rates by slightly increasing computational complexity.Both networks are finally implemented in a custom embedded multiprocessor, verifying that theorical and measured gains from PCA are consistent. Furthermore, parallelizing the PCA compressed network over 8 PEs achieves a x11.68 speed-up with respect to the original network running on a single PE
He, Bing. "Estimation paramétrique du signal par réseaux de neurones." Lille 1, 2002. https://pepite-depot.univ-lille.fr/RESTREINT/Th_Num/2002/50376-2002-75.pdf.
Повний текст джерелаDabo, Yakouba. "Dynamique de formation de réseaux cellulaires en solidification dirigée d'́alliages binaires." Aix-Marseille 3, 2001. http://www.theses.fr/2001AIX30112.
Повний текст джерелаThis thesis is devoted to the study of cellular array dynamics, during directional solidification of binary alloys in bulk samples: Bi-lwt%Sb and Al-l,5wt% Ni. On Bi-Iwt% Sb alloys, a preliminary analysis is carried ont on solute microsegregation induced by PID (Peltier interface demarcation). Then, the evolution of solidification microstructure is investigated The fondamental role of interface kinetics on thermoelectric demarcation is established in particular during the first instants of the electric pulse. Using PID, we analyze the evolution of interface microstructure following the morphological destabilization of the planar front. During the first stages of this destabilization. We observe the existence of two wavelengths. Progressively, a partially faceted cell array (facet linked by rough tops and bottoms) develops. Cell instabilities are observed specially on facets The origin of these instabilities is discussed in the framework of thé spreading out of crystalline steps along the facets. By a comparative study of AI-l,5wt% Ni alloys solidified on ground and in microgravity we show that convection is at the origin of a strong distortion of the solidification front in ground-based samples. .
Lebbah, Mustapha. "Carte topologique pour données qualitatives : application à la reconnaissance automatique de la densité du trafic routier." Phd thesis, Université de Versailles-Saint Quentin en Yvelines, 2003. http://tel.archives-ouvertes.fr/tel-00161698.
Повний текст джерелаCe travail de thèse a été réalisé à la direction de la recherche de RENAULT. Le travail s'est focalisé sur le développement d'un modèle de reconnaissance de trafic.
Le premier modèle proposé dans cette thèse est dédié aux données binaires ''BTM''. C'est un modèle de quantification vectorielle de type carte topologique. Ce modèle prend les caractéristiques principales des cartes topologiques. Pour ce nouveau modèle, afin de prendre en compte les spécificités des données binaires, on a choisi de changer la métrique usuelle utilisée dans les modèles de cartes topologiques et d'utiliser la distance Hamming.
Le second modèle est le premier modèle probabiliste de cartes topologiques dédié aux données binaires. Ce modèle s'inspire de travaux antérieurs qui modélisent une distribution par un mélange de mélange de lois de Bernoulli.
Le troisième modèle est un nouveau modèle de carte topologique appelé CTM (Categorical topological Map) adapté à la classification non supervisée des données qualitatives multi-dimensionnelles. Ce nouveau modèle conserve cependant les principales caractéristiques des deux modèles précédents. Afin de maximiser les vraisemblance des données, CTM utilise de manière classique l'algorithme EM.
Dans ce mémoire, on introduit le domaine d'application propre au travail mené chez RENAULT. Nous détaillerons l'apport original de notre travail: utilisation de l'information catégorielle pour traiter de la reconnaissance du trafic. Nous exposerons les différentes analyses effectuées sur l'application des algorithmes proposés.
Bénédic, Yohann. "Approche analytique pour l'optimisation de réseaux de neurones artificiels." Phd thesis, Université de Haute Alsace - Mulhouse, 2007. http://tel.archives-ouvertes.fr/tel-00605216.
Повний текст джерелаGatet, Laurent. "Intégration de Réseaux de Neurones pour la Télémétrie Laser." Phd thesis, Toulouse, INPT, 2007. http://oatao.univ-toulouse.fr/7595/1/gatet.pdf.
Повний текст джерелаRobitaille, Benoît. "Contrôle adaptatif par entraînement spécialisé de réseaux de neurones." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ35778.pdf.
Повний текст джерелаDucom, Jean-Christophe. "Codage temporel et apprentissage dans les réseaux de neurones." Aix-Marseille 1, 1996. http://www.theses.fr/1996AIX11041.
Повний текст джерелаChakik, Fadi El. "Maximum d'entropie et réseaux de neurones pour la classification." Grenoble INPG, 1998. http://www.theses.fr/1998INPG0091.
Повний текст джерелаBenaïm, Michel. "Dynamiques d'activation et dynamiques d'apprentissage des réseaux de neurones." Toulouse, ENSAE, 1992. http://www.theses.fr/1992ESAE0001.
Повний текст джерелаAupetit, Michaël. "Approximation de variétés par réseaux de neurones auto-organisés." Grenoble INPG, 2001. http://www.theses.fr/2001INPG0128.
Повний текст джерелаJiang, Fei. "Optimisation de la topologie de grands réseaux de neurones." Paris 11, 2009. http://www.theses.fr/2009PA112211.
Повний текст джерелаIn this dissertation, we present our study regarding the influence of the topology on the learning performances of neural networks with complex topologies. Three different neural networks have been investigated: the classical Self-Organizing Maps (SOM) with complex graph topology, the Echo States Network (ESN) and the Standard Model Features(SMF). In each case, we begin by comparing the performances of different topologies for the same task. We then try to optimize the topology of some neural network in order to improve such performance. The first part deals with Self-Organizing Maps, and the task is the standard classification of handwritten digits from the MNIST database. We show that topology has a small impact on performance and robustness to neuron failures, at least at long learning times. Performance may however be increased by almost 10% by artificial evolution of the network topology. In our experimental conditions, the evolved networks are more random than their parents, but display a more heterogeneous degree distribution. In the second part, we propose to apply CMA-ES, the state-of-the-art method in evolutionary continuous parameter optimization, to the evolutionary learning of the parameters of an Echo State Network (the Readout weights, of course, but also, Spectral Radius, Slopes of the neurons active function). First, a standard supervised learning problem is used to validate the approach and compare it to the original one. But the flexibility of Evolutionary optimization allows us to optimize not only the outgoing weights but also, or alternatively, other ESN parameters, sometimes leading to improved results. The classical double pole balancing control problem is used to demonstrate the feasibility of evolutionary reinforcement learning of ESN. We show that the evolutionary ESN obtain results that are comparable with those of the best topology-learning neuro-evolution methods. Finally, the last part presents our initial research of the SMF - a visual object recognition model which is inspired by the visual cortex. Two version based on SMF are applied to the PASCAL Visual multi-Object recognition Challenge (VOC2008). The long terms goal is to find the optimal topology of the SMF model, but the computation cost is however too expensive to optimize the complete topology directly. So as a first step, we apply an Evolutionary Algorithm to auto-select the feature used by the systems. We show that, for the VOC2008 challenge, with only 20% selected feature, the system can perform as well as with all 1000 randomly selected feature
Alvado, Ludovic. "Neurones artificiels sur silicium : une évolution vers les réseaux." Bordeaux 1, 2003. http://www.theses.fr/2003BOR12674.
Повний текст джерелаThis thesis describes a new approach for modelling biological neuron networks. This approach uses analogue specific integrated circuit (ASIC) in which Hodgkin-Huxley formalism as been implemented to integrate medium density artificial neural network, modelled at a biological realistic level. This thesis also deals with the component mismatches problem and the pertinent choice of optimized structure dedicated to network applications
Biela, Philippe. "Classification automatique d'observations multidimensionnelles par réseaux de neurones compétitifs." Lille 1, 1999. https://pepite-depot.univ-lille.fr/LIBRE/Th_Num/1999/50376-1999-469.pdf.
Повний текст джерелаElhor, Noureddine. "Suivi de fonctionnement d'une éolienne par réseaux de neurones." Lille 1, 2000. https://pepite-depot.univ-lille.fr/LIBRE/Th_Num/2000/50376-2000-57.pdf.
Повний текст джерелаLa projection non lineaire offre une visualisation plane des donnees alors que les comparaisons entre les sorties estimees et reelles generent des residus permettant de verifier si le spectre courant a devie ou non du fonctionnement normal memorise par le reseau. Pour valider notre etude, nous avons teste les performances du reseau sur des donnees en fonctionnement normal acquis dans des situations differentes de celles de la base d'apprentissage et sur des situations correspondant a des defauts simules type balourd. Ce type de defauts se manifeste par l'apparition d'une frequence et ses harmoniques dans le spectre d'energie. Dans les deux situations, les resultats obtenus par le reseau ont ete quantifies et se sont reveles satisfaisants. Nous avons exploite, par ailleurs, un reseau modulaire a apprentissage supervise pour la discrimination entre deux situations : le fonctionnement normal et la presence d'un defaut type balourd d'une amplitude minimale fixee. Les performances du reseau ont ete testees sur des defauts d'amplitudes differentes. Souvent les methodes de diagnostic sont appliquees sur des bancs d'essais dans des conditions de laboratoire controlees. Notre demarche est d'autant plus importante que nous surveillons une machine reelle en pleine production
Oussar, Yacine. "Réseaux d'ondelettes et réseaux de neurones pour la modélisation statique et dynamique de processus." Phd thesis, Université Pierre et Marie Curie - Paris VI, 1998. http://pastel.archives-ouvertes.fr/pastel-00000677.
Повний текст джерелаBissery, Christophe. "La détection centralisée des fuites sur les réseaux d'eau potable par réseaux de neurones." Lyon, INSA, 1994. http://www.theses.fr/1994ISAL0112.
Повний текст джерелаFor few years, under the influence of the urban environment, the perception of dysfunction risk in technical systems and in particular in water supply networks has changed. The lack of risk doesn't exist and it's necessary to learn how to manage it. It's in this way that appears the need of centralized leakage detection on water supply networks, leaks that represent an important part of the dysfunction risk of water supply. This study proposes a centralized leakage detection system using a computerized neural network approach. The building method of learning bases and the sensors localization method are pointed out and developed. This study has showed that on a realistic network model results obtained with the centralized leakage detection system using a computerized neural network approach allowed experimentations on real networks. The study ends on the presentation of the working priorities for these real experimentations (and in particular the need of hourly water consumption previsions)
Basterrech, Sebastián. "Apprentissage avec les réseaux de neurones aléatoires et les machines de calcul avec réservoir de neurones." Rennes 1, 2012. http://www.theses.fr/2012REN1S178.
Повний текст джерелаSince the 1980s a new computational model merging concepts from neural networks and queuing theory was developed. The model was introduced under the name of Random Neural Networks (RNNs), inside the field of Neural Networks. In this thesis, a first contribution consists of an adaptation of quasi-Newton optimisation methods for training the RNN model. In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). One of the pioneers and most diffused RC methods is the Echo State Network (ESN) model. Here, we propose a method based on topographic maps to initialise the ESN procedure. Another contribution of the thesis is the introduction of a new RC model called the Echo State Queueing Network (ESQN), where we use ideas coming from RNNs for the design of the reservoir. An ESQN consists of an ESN where the reservoir has a new dynamics inspired by recurrent RNNs. In this thesis, we position the ESQN method in the global Machine Learning area, and provide examples of their use and performances. Finally, we propose a method for real–time estimation of Speech Quality using the learning tools above described. Audio quality in the Internet can be strongly affected by network conditions. As a consequence, many techniques to evaluate it have been developed. In particular, the ITU-T adopted in 2001 a technique called Perceptual Evaluation of Speech Quality (PESQ) to automatically measuring speech quality. PESQ is a well-known and widely used procedure, providing in general an accurate evaluation of perceptual quality by comparing the original and received voice sequences. The thesis provides a procedure for estimating PESQ output working only with measures taken on the network state and using some properties of the communication system, without any original signal. The experimental results obtained prove the capability of our approach to give good estimations of the speech quality in a real–time context
Roth, Sophie. "Réseaux de neurones modèles : contrôle de la différenciation axonale par micropatterns." Phd thesis, Grenoble 1, 2009. http://www.theses.fr/2009GRE10262.
Повний текст джерелаIn vitro neuronal networks are pertinent simple systems to approach brain computational complexity. They are even more useful when an architecture evoking the in vivo network organization can be enforced. This is why micro-patterned substrates are now widely used to force cell adhesion and growth according to a predefined topology. However, building fully controlled neuronal microcircuits requires precise supervision of the information flow between cells, which can only be achieved by inducing neuronal polarity (i. E. Axonal differentiation) in a specific direction. Although more polarity-regulating molecules are being discovered each day, they are hardly usable to create polarized networks in vitro as most patterning technologies are not compatible with protein grafting. In this PhD, our goal was to achieve full control of neural polarity by combined action of non-specific adhesion and physical constraints provided by sophisticated patterning geometries. We report here the mastering of axonal growth direction with a success close to 90\%. This result was based upon previous observations : the centrosome localization determines the axon emergence point and mechanical tension is sufficient to ensure axon formation. We coupled these results into a single pattern to constrain the centrosome position with suitable adhesive patterns and prevent axon growing on undesired positions with specific curved shapes that provide a limitation of neurite tension. These findings not only provide an important tool for creating neuronal model networks but also question the centrosome function and the mechanisms of adhesion and force transmission within neurites that have been so far neglected in favour of growth cone analysis
Krauth, Werner. "Physique statistique des réseaux de neurones et de l'optimisation combinatoire." Phd thesis, Université Paris Sud - Paris XI, 1989. http://tel.archives-ouvertes.fr/tel-00011866.
Повний текст джерелаPersonnaz, Léon. "Etude des réseaux de neurones formels : conception, propriétés et applications." Paris 6, 1986. http://www.theses.fr/1986PA066569.
Повний текст джерелаMercier, David. "Hétéro-association de signaux audio-vidéo par réseaux de neurones." Rennes 1, 2003. http://www.theses.fr/2003REN10009.
Повний текст джерелаPuechmorel, Stéphane. "Réseaux de neurones et optimisation globale en analyse temps-fréquence." Toulouse, INPT, 1994. http://www.theses.fr/1994INPT105H.
Повний текст джерелаBoné, Romuald. "Réseaux de neurones récurrents pour la prévision de séries temporelles." Tours, 2000. http://www.theses.fr/2000TOUR4003.
Повний текст джерелаStrock, Anthony. "Mémoire de travail dans les réseaux de neurones récurrents aléatoires." Thesis, Bordeaux, 2020. http://www.theses.fr/2020BORD0195.
Повний текст джерелаWorking memory can be defined as the ability to temporarily store and manipulate information of any kind.For example, imagine that you are asked to mentally add a series of numbers.In order to accomplish this task, you need to keep track of the partial sum that needs to be updated every time a new number is given.The working memory is precisely what would make it possible to maintain (i.e. temporarily store) the partial sum and to update it (i.e. manipulate).In this thesis, we propose to explore the neuronal implementations of this working memory using a limited number of hypotheses.To do this, we place ourselves in the general context of recurrent neural networks and we propose to use in particular the reservoir computing paradigm.This type of very simple model nevertheless makes it possible to produce dynamics that learning can take advantage of to solve a given task.In this job, the task to be performed is a gated working memory task.The model receives as input a signal which controls the update of the memory.When the door is closed, the model should maintain its current memory state, while when open, it should update it based on an input.In our approach, this additional input is present at all times, even when there is no update to do.In other words, we require our model to be an open system, i.e. a system which is always disturbed by its inputs but which must nevertheless learn to keep a stable memory.In the first part of this work, we present the architecture of the model and its properties, then we show its robustness through a parameter sensitivity study.This shows that the model is extremely robust for a wide range of parameters.More or less, any random population of neurons can be used to perform gating.Furthermore, after learning, we highlight an interesting property of the model, namely that information can be maintained in a fully distributed manner, i.e. without being correlated to any of the neurons but only to the dynamics of the group.More precisely, working memory is not correlated with the sustained activity of neurons, which has nevertheless been observed for a long time in the literature and recently questioned experimentally.This model confirms these results at the theoretical level.In the second part of this work, we show how these models obtained by learning can be extended in order to manipulate the information which is in the latent space.We therefore propose to consider conceptors which can be conceptualized as a set of synaptic weights which constrain the dynamics of the reservoir and direct it towards particular subspaces; for example subspaces corresponding to the maintenance of a particular value.More generally, we show that these conceptors can not only maintain information, they can also maintain functions.In the case of mental arithmetic mentioned previously, these conceptors then make it possible to remember and apply the operation to be carried out on the various inputs given to the system.These conceptors therefore make it possible to instantiate a procedural working memory in addition to the declarative working memory.We conclude this work by putting this theoretical model into perspective with respect to biology and neurosciences
Chevallier, Julien. "Modélisation de grands réseaux de neurones par processus de Hawkes." Thesis, Université Côte d'Azur (ComUE), 2016. http://www.theses.fr/2016AZUR4051/document.
Повний текст джерелаHow does the brain compute complex tasks? Is it possible to create en artificial brain? In order to answer these questions, a key step is to build mathematical models for information processing in the brain. Hence this manuscript focuses on biological neural networks and their modelling. This thesis lies in between three domains of mathematics - the study of partial differential equations (PDE), probabilities and statistics - and deals with their application to neuroscience. On the one hand, the bridges between two neural network models, involving two different scales, are highlighted. At a microscopic scale, the electrical activity of each neuron is described by a temporal point process. At a larger scale, an age structured system of PDE gives the global activity. There are two ways to derive the macroscopic model (PDE system) starting from the microscopic one: by studying the mean dynamics of one typical neuron or by investigating the dynamics of a mean-field network of $n$ neurons when $n$ goes to infinity. In the second case, we furthermore prove the convergence towards an explicit limit dynamics and inspect the fluctuations of the microscopic dynamics around its limit. On the other hand, a method to detect synchronisations between two or more neurons is proposed. To do so, tests of independence between temporal point processes are constructed. The level of the tests are theoretically controlled and the practical validity of the method is illustrated by a simulation study. Finally, the method is applied on real data
Pothier, Dominique. "Réseaux convolutifs à politiques." Master's thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/69184.
Повний текст джерелаDespite their excellent performances, artificial neural networks high demand of both data and computational power limit their adoption in many domains. Developing less demanding architecture thus remain an important endeavor. This thesis seeks to produce a more flexible and less resource-intensive architecture by using reinforcement learning theory. When considering a network as an agent instead of a function approximator, one realize that the implicit policy followed by popular feed forward networks is extremely simple. We hypothesize that an architecture able to learn a more flexible policy could reach similar performances while reducing its resource footprint. The architecture we propose is inspired by research done in weight prediction, particularly by the hypernetwork architecture, which we use as a baseline model.Our results show that learning a dynamic policy achieving similar results to the static policies of conventional networks is not a trivial task. Our proposed architecture succeeds in limiting its parameter space by 20%, but does so at the cost of a 24% computation increase and loss of5% accuracy. Despite those results, we believe that this architecture provides a baseline that can be improved in multiple ways that we describe in the conclusion.
Koubi, Vassilada. "Reseaux de neurones et optimisation combinatoire." Paris 5, 1994. http://www.theses.fr/1994PA05S014.
Повний текст джерелаRichard, Vincent. "Outils de synthèse pour les réseaux réflecteurs exploitant la cellule Phoenix et les réseaux de neurones." Thesis, Rennes, INSA, 2018. http://www.theses.fr/2018ISAR0004/document.
Повний текст джерелаIn collaboration with Thales Alenia Space and the French Space Agency (CNES), this PHD takes part in a very active international context on a new antenna: the reflectarrays (RA).Combining the advantages of conventional reflectors and those of networks, RA could eventually replace the currently used shaped reflectors. They consist of a primary source placed in front of a network of cells controlling the properties of the reflected electromagnetic field. Although many studies already focus on the characterization of cells, one of the issues is to carefully select them to achieve the final antenna: this is the synthesis step.An overview of different synthesis methods revealed the complexity to quickly obtain good performance simultaneously on the co- and cross-polarizations, for a wide frequency band and for the realization of shaped radiation pattern. The Phoenix cell is selected in this work for its good properties since it provides the entire phase range following a continuous cycle of geometries.Because one of the constraints in the design of RA is to maintain continuous geometry variations between two juxtaposed cells on the layout, a spherical representation tool made it possible to classify all the studied cells. It judiciously lists all the cells on a continuous, closed and periodic surface.A new step is reached with the design of behavioral models using Artificial Neural Networks (ANN). These models enable to a fast electromagnetic characterization of cells in terms of phase and amplitude of the direct and cross coefficients of the reflection matrix.The originality of the synthesis algorithm proposed in this work is the combined use of the spherical representation and a rapid cell characterization by ANN. A min / max optimization tool is used to improve the overall performance of the RA panel. It is then applied to a concrete case as part of a telecommunication mission
Aziz, Mohammed, and Abdelaziz Bensrhair. "Apprentissage de réseaux de neurones impulsionnels. Application à des systèmes sensorimoteurs." INSA de Rouen, 2005. http://www.theses.fr/2005ISAM0005.
Повний текст джерелаBernauer, Éric. "Les réseaux de neurones et l'aide au diagnostic : un modèle de neurones bouclés pour l'apprentissage de séquences temporelles." Toulouse 3, 1996. http://www.theses.fr/1996TOU30277.
Повний текст джерелаWang, Shengrui. "Réseaux multicouches de neurones artificiels : algorithmes d'apprentissage, implantations sur hypercube : applications." Phd thesis, Grenoble INPG, 1989. http://tel.archives-ouvertes.fr/tel-00335818.
Повний текст джерелаChabot, Florian. "Analyse fine 2D/3D de véhicules par réseaux de neurones profonds." Thesis, Université Clermont Auvergne (2017-2020), 2017. http://www.theses.fr/2017CLFAC018/document.
Повний текст джерелаIn this thesis, we are interested in fine-grained analysis of vehicle from an image. We define fine-grained analysis as the following concepts : vehicle detection in the image, vehicle viewpoint (or orientation) estimation, vehicle visibility characterization, vehicle 3D localization and make and model recognition. The design of reliable solutions for fine-grained analysis of vehicle open the door to multiple applications in particular for intelligent transport systems as well as video surveillance systems. In this work, we propose several contributions allowing to address partially or wholly this issue. Proposed approaches are based on joint deep learning technologies and 3D models. In a first section, we deal with make and model classification keeping in mind the difficulty to create training data. In a second section, we investigate a novel method for both vehicle detection and fine-grained viewpoint estimation based on local apparence features and geometric spatial coherence. It uses models learned only on synthetic data. Finally, in a third section, a complete system for fine-grained analysis is proposed. It is based on the multi-task concept. Throughout this report, we provide quantitative and qualitative results. On several aspects related to vehicle fine-grained analysis, this work allowed to outperform state of the art methods
Roth, Sophie. "Réseaux de Neurones modèles : Contrôle de la différenciation axonale par micropatterns." Phd thesis, Université de Grenoble, 2009. http://tel.archives-ouvertes.fr/tel-01010302.
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