Dissertations / Theses: 'Hopfield network; Neural networks'

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Brouwer, Roelof K. "Pattern recognition using a generalised discrete Hopfield network." Thesis, University of Warwick, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307974.

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Smeda, Adel Abdullah. "Application of the Hopfield neural network in routing for computer networks." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ39701.pdf.

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Tseng, Hung-Li. "Computational Complexity of Hopfield Networks." Thesis, University of North Texas, 1998. https://digital.library.unt.edu/ark:/67531/metadc278272/.

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There are three main results in this dissertation. They are PLS-completeness of discrete Hopfield network convergence with eight different restrictions, (degree 3, bipartite and degree 3, 8-neighbor mesh, dual of the knight's graph, hypercube, butterfly, cube-connected cycles and shuffle-exchange), exponential convergence behavior of discrete Hopfield network, and simulation of Turing machines by discrete Hopfield Network.

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Cheng, Chih Kang. "Hardware implementation of the complex Hopfield neural network." CSUSB ScholarWorks, 1995. https://scholarworks.lib.csusb.edu/etd-project/1016.

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Breese, Amanda Jane. "Optics, optimisation and the Hopfield neural network." Thesis, University of Reading, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303131.

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Halabian, Faezeh. "An Enhanced Learning for Restricted Hopfield Networks." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42271.

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This research investigates developing a training method for Restricted Hopfield Network (RHN) which is a subcategory of Hopfield Networks. Hopfield Networks are recurrent neural networks proposed in 1982 by John Hopfield. They are useful for different applications such as pattern restoration, pattern completion/generalization, and pattern association. In this study, we propose an enhanced training method for RHN which not only improves the convergence of the training sub-routine, but also is shown to enhance the learning capability of the network. Particularly, after describing the architecture/components of the model, we propose a modified variant of SPSA which in conjunction with back-propagation over time result in a training algorithm with an enhanced convergence for RHN. The trained network is also shown to achieve a better memory recall in the presence of noisy/distorted input. We perform several experiments, using various datasets, to verify the convergence of the training sub-routine, evaluate the impact of different parameters of the model, and compare the performance of the trained RHN in recreating distorted input patterns compared to conventional RBM and Hopfield network and other training methods.

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Luo, Dexiang. "Occluded object discrimination by a modified Hopfield neural network." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0020/MQ55520.pdf.

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Garcia, Garcia Núria 1958. "Radio Resource Management strategies based hopfield neural networks." Doctoral thesis, Universitat Pompeu Fabra, 2009. http://hdl.handle.net/10803/7556.

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Esta tesis doctoral se enmarca en la temática de Gestion de Recursos Radioelectricos en los sistemas de comunicaciones móviles, 3G, B3G y 4G. Como es ampliamente conocido los recursos radios son escasos, particularmente en los sistemas de comunicaciones móviles celulares, y en consecuencia es de uso obligado una gestión eficiente de los mismos. Desde un punto de vista práctico si bien estos sistemas se basan en el uso de tecnologías de acceso radio estandarizada, no es este el caso para los algoritmos subyacentes a la gestión de los recursos radio, de manera que siempre son posibles nuevas realizaciones de los mismos que resulten más convenientes en el marco del estándar en cuestión. Es en esta dirección hacia donde apunta la realización de esta tesis doctoral, y entiendo que lo consigue con éxito al introducir nuevas estrategias de Gestion de los Recursos Radio en el marco de las estrategias de múltiple acceso CDMA en los sistemas 3G, TDMA/CDMA en los sistemas B3G y OFDMA en los sistemas 4G. Esta tesis, tras identificar los gestores más usuales de gestión de recursos radio y una breve descripción de los mismos, introduce una descripción básicamente autocontenida de los aspectos más relevantes de los sistemas de acceso múltiple WCDMA y OFDMA. En este sentido se detallan mecanismos de su funcionamiento que con posterioridad serán utilizados en la definición y especificación de los algoritmos de gestión de recursos propiamente dichos. Con posterioridad se hace un breve recorrido sobre lo que son las redes neuronales , para finalizar en una exposición más detallada de las Redes Neuronales de Hopfield que serán el hilo conductor de los trabajos de esta tesis. En particular se describen las ecuaciones que caracterizan estas redes como sistemas dinámicos y se establecen sus condiciones de convergencia a través de los teoremas de estabilidad Lyapunov y la definición de la función Energía.De la conjunción de las particularidades de los sistemas de acceso WCDMA, TDMA y OFDMA y de las redes neuronales de Hopfield se van desarrollando una serie de algoritmos que operan en escenarios unicelulares y que entiendo novedosos y que a continuación enumeran brevemente.Admisión en un sistema WCDMA , enlace ascendente, mediante una gestión optimizada de las distintas velocidades de transmisión asignadas a los usuarios que comparten el acceso y que se les permite distintos perfiles. Aspectos relativos a la robustez del algoritmo, y en particular a su convergencia son también detallados. Se suponen restricciones de carga de la red máxima, repartición del espectro justa y potencia máxima disponible en los terminales móviles. Se suponen un servicio en tiempo real con velocidades variables. La probabilidad de bloqueo se usa para exhibir las prestaciones del algoritmo.Gestión de las velocidades de los usuarios ya admitidos en un sistema WCDMA,enlace ascendente, con objeto de garantizarles una definida probabilidad de satisfacción superior a un determinado valor y que está basada en las velocidades reales de transmisión asignadas. Se supone también un servicio en tiempo real con velocidades variables y las mismas restricciones que en Admisión. Gestión de las velocidades de los usuarios ya admitidos en un sistema WCDMA, enlace descendente, con objeto de garantizarles un máximo retardo en la entrega de paquetes. Se suponen restricciones de repartición del espectro justa y potencia máxima disponible en la estación de base. Se supone un servicio interactivo basado en un modelo de tráfico para servicios www. Se introduce también un algoritmo de referencia a efectos comparativos. La probabilidad de pérdida es el parámetro usado para valorar las prestaciones del algoritmo.Gestión combinada de servicios en tiempo real e interactivos en sistemas WCDMA, enlace descendente. Incorpora parte de los algoritmos anteriormente enunciados y se mantienen los mismos modelos de tráfico y las mismas restricciones. Se han usado en esta caso las probabilidades de satisfacción y de pérdida para capturar el la velocidad de transmisión agregada y retardo respectivamente Algoritmo de Gestión común de recursos radio para un escenario B3G donde un usuario puede ser servido por más de un acceso. En este caso se han usado WCDMA y TDMA. Algoritmos de Gestión de las velocidades de los usuarios ya admitidos en un sistema OFDMA, enlace descendente, con objeto de garantizarles un máximo retardo en la entrega de paquetes.La tesis apunta también hacia prometedoras futuras líneas de investigación que pretenden explotar la base de la metodología desarrollada en esta tesis y que consisten en escenarios celulares centralizadas para pasar después a distribuidas en entornos multicelulares y en particular para los sistemas OFDMA , base de los accesos en 4G.

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Bireddy, Chakradhar. "Hopfield Networks as an Error Correcting Technique for Speech Recognition." Thesis, University of North Texas, 2004. https://digital.library.unt.edu/ark:/67531/metadc5551/.

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I experimented with Hopfield networks in the context of a voice-based, query-answering system. Hopfield networks are used to store and retrieve patterns. I used this technique to store queries represented as natural language sentences and I evaluated the accuracy of the technique for error correction in a spoken question-answering dialog between a computer and a user. I show that the use of an auto-associative Hopfield network helps make the speech recognition system more fault tolerant. I also looked at the available encoding schemes to convert a natural language sentence into a pattern of zeroes and ones that can be stored in the Hopfield network reliably, and I suggest scalable data representations which allow storing a large number of queries.

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Barron, Kenneth Falconer. "Optics in Hopfield content addressable memories." Thesis, Heriot-Watt University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.335137.

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Riesenberg, John R. "CATASTROPHIC FORGETTING IN NEURAL NETWORKS." University of Cincinnati / OhioLINK, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=ucin976117376.

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Wang, Ziqing. "Fuzzy neural network for edge detection and Hopfield network for edge enhancement." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0005/MQ42458.pdf.

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Calmese, Ife D. "The proteretic Hopfield neural network analog to digital converter /." Available to subscribers only, 2008. http://proquest.umi.com/pqdweb?did=1674094131&sid=4&Fmt=2&clientId=1509&RQT=309&VName=PQD.

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Thesis (Ph. D.)--Southern Illinois University Carbondale, 2008.
"Department of Electrical and Computer Engineering." Keywords: Hopfield neural network, Analog to digital converter. Includes bibliographical references (p. 47-48). Also available online.

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Calmese, Ife. "The Proteretic Hopfield Neural Network Analog to Digital Converter." OpenSIUC, 2008. https://opensiuc.lib.siu.edu/dissertations/255.

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The Hopfield neuron with predictive hysteresis is proposed and the efficiency of employing these neurons in analog to digital conversion, via the Hopfield Neural Network, will be demonstrated. Traditional hysteresis is defined, generally, as the occurrence of a delayed effect when forces acting on an object are varied. It will be shown that predictive hysteresis, a type of reverse hysteresis, improves upon the speed of hysteretic and non hysteretic systems without compromising the accuracy.

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Borundiya, Amit Parasmal. "Implementation of Hopfield Neural Network Using Double Gate MOSFET." Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1204910134.

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Valiveti, Natana Carleton University Dissertation Computer Science. "Parallel computational geometry on Analog Hopfield Networks." Ottawa, 1992.

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Corrêa, Leonardo Garcia. "Memória associativa em redes neurais realimentadas." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-06122004-115632/.

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Nessa dissertação, é investigado o armazenamento e a recuperação de padrões de forma biologicamente inspirada no cérebro. Os modelos estudados consistiram de redes neurais realimentadas, que tentam modelar certos aspectos dinâmicos do funcionamento do cérebro. Em particular, atenção especial foi dada às Redes Neurais Celulares, que constituem uma versão localmente acoplada do já clássico modelo de Hopfield. Além da análise de estabilidade das redes consideradas, foi realizado um teste com o intuito de avaliar o desempenho de diversos métodos de construção de memórias endereçáveis por conteúdo (memórias associativas) em Redes Neurais Celulares.
In this dissertation we investigate biologically inspired models of pattern storage and retrieval, by means of feedback neural networks. These networks try to model some of the dynamical aspects of brain functioning. The study concentrated in Cellular Neural Networks, a local coupled version of the classical Hopfield model. The research comprised stability analysis of the referred networks, as well as performance tests of various methods for content-addressable (associative) memory design in Cellular Neural Networks.

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Martínez, Marrodán Diego. "Automatic Scheduling for schools : Scalability of the Hopfield Neural Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166589.

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This project is focused on evaluating, in terms of real time needed to find a solution, the scalability of Hopfield Neural Networks, a Machine Learning method, applied to a common problem that every educational institution has to deal with at least once in every academic year, timetabling. With this purpose, the problem is first introduced. And secondly, in the background, the concept of "constraint" is presented, to continue with a brief explanation of Artificial Neural Networks, the state of the art and more specifically, how Hopfield Neural Networks are characterized. The formulation, modifications used, and the algorithm are presented. This algorithm will be implemented in MATLAB, and it will be run on data sets of different sizes. The results obtained for the presented data sets are presented in a table and graphs, to later discuss these results. In this discussion, it is found that the time spent to get a solution could scale quadratically with respect to the size of the problem, but there is not statistical evidence to this hypothesis. Finally, the conclusion is that Hopfield Neural Networks could have a good scalability if the hypothesis worked for bigger data sets, and some future work in the field is presented, like using sparse matrices for the implementation of the problem, or studying the scalability of Hopfield Neural Networks in other kinds of scheduling.

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Sousa, Fabiano Berardo de. "Análise de modelo de Hopfield com topologia de rede complexa." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-30012014-111520/.

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Redes neurais biológicas contêm bilhões de células (neurônios) agrupadas em regiões espacial e funcionalmente distintas. Elas também apresentam comportamentos complexos, tais como dinâmicas periódicas e caóticas. Na área da Inteligência Artificial, pesquisas mostram que Redes Neurais Caóticas, isto é, modelos de Redes Neurais Artificiais que operam com dinâmicas complexas, são mais eficientes do que modelos tradicionais no que diz respeito a evitar memórias espúrias. Inspirado pelo fato de que o córtex cerebral contém agrupamentos de células e motivado pela eficiência no uso de dinâmicas complexas, este projeto de pesquisa investiga o comportamento dinâmico de um modelo de Rede Neural Artificial Recorrente, como o de Hopfield, porém com a topologia sináptica reorganizada a ponto de originar agrupamentos de neurônios, tal como acontece em uma Rede Complexa quando esta apresenta uma estrutura de comunidades. O modelo de treinamento tradicional de Hopfield também é alterado para uma regra de aprendizado que posta os padrões em ciclos, gerando uma matriz de pesos assimétrica. Resultados indicam que o modelo proposto oscila entre comportamentos periódicos e caóticos, dependendo do grau de fragmentação das sinapses. Com baixo grau de fragmentação, a rede opera com dinâmica periódica, como consequência da regra de treinamento utilizada. Dinâmicas caóticas parecem surgir quando existe um alto grau de fragmentação. Mostra-se, também, que é possível obter caoticidade em uma topologia adequadamente modular, ou seja, como uma estrutura de comunidades válida. Desta forma, este projeto de pesquisa provê uma metodologia alternativa para se construir um modelo de Rede Neural Artificial que realiza tarefas de reconhecimento de padrões, explorando dinâmicas complexas por meio de uma estrutura de conexões que se mostra mais similar à topologia existente no cérebro
Biological neural networks contain billions of neurons divided in spatial and functional clusters to perform dierent tasks. It also operates with complex dynamics such as periodic and chaotic ones. It has been shown that Chaotic Neural Networks are more efficient than conventional recurrent neural networks in avoiding spurious memory. Inspired by the fact that the cerebral cortex has speficic groups of cells and motivated by the efficiency of complex behaviors, in this document we investigate the dynamics of a recurrent neural network, as the Hopfield one, but with neurons coupled in such a way to form a complex network community structure. Also, we generate an asymmetric weight matrix placing pattern cycles during learning. Our study shows that the network can operate with periodic and chaotic dynamics, depending on the degree of the connection\'s fragmentation. For low fragmentation degree, the network operates with periodic dynamic duo to the employed learning rule. Chaotic behavior seems to rise for a high fragmentation degree. We also show that the neural network can hold both chaotic dynamic and a high value of modularity measure at the same time, indicating an acceptable community structure. These findings provide an alternative way to design dynamical neural networks to perform pattern recognition tasks exploiting periodic and chaotic dynamics by using a more similar topology to the topology of the brain

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Nguyen, Quang Minh. "Super-resolution mapping using the Hopfield neural network with supplementary data." Thesis, University of Southampton, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.437885.

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McCallum, Simon, and n/a. "Catastrophic forgetting and the pseudorehearsal solution in Hopfield networks." University of Otago. Department of Computer Sciences, 2007. http://adt.otago.ac.nz./public/adt-NZDU20080130.105101.

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Most artificial neural networks suffer from the problem of catastrophic forgetting, where previously learnt information is suddenly and completely lost when new information is learnt. Memory in real neural systems does not appear to suffer from this unusual behaviour. In this thesis we discuss the problem of catastrophic forgetting in Hopfield networks, and investigate various potential solutions. We extend the pseudorehearsal solution of Robins (1995) enabling it to work in this attractor network, and compare the results with the unlearning procedure proposed by Crick and Mitchison (1983). We then explore a familiarity measure based on the energy profile of the learnt patterns. By using the ratio of high energy to low energy parts of the network we can robustly distinguish the learnt patterns from the large number of spurious "fantasy" patterns that are common in these networks. This energy ratio measure is then used to improve the pseudorehearsal solution so that it can store 0.3N patterns in the Hopfield network, significantly more than previous proposed solutions to catastrophic forgetting. Finally, we explore links between the mechanisms investigated in this thesis and the consolidation of newly learnt material during sleep.

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Akbar, Shahzad. "Vibration design by means of structural modification." Thesis, Brunel University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.263499.

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Banks, Jess M. "Chaos and Learning in Discrete-Time Neural Networks." Oberlin College Honors Theses / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1445945609.

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Storkey, Amos James. "Efficient covariance matrix methods for Bayesian Gaussian processes and Hopfield neural networks." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313335.

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KAIMAL, VINOD GOPALKRISHNA. "A NEURAL METHOD OF COMPUTING OPTICAL FLOW BASED ON GEOMETRIC CONSTRAINTS." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1037632137.

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Meins, Nils [Verfasser], and Stefan [Akademischer Betreuer] Wermter. "Diversity-driven Hopfield Neural Network Ensembles for Face Detection / Nils Meins ; Betreuer: Stefan Wermter." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2019. http://d-nb.info/1192442679/34.

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Tatem, Andrew. "Super-resolution land cover mapping from remotely sensed imagery using a Hopfield neural network." Thesis, University of Southampton, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395911.

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Cote, Stephane. "Measurement of sea-surface velocities from satellite sensor images using the Hopfield neural network." Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242549.

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Lindblad, Maria. "A Comparison of a Heuristic and a Hopfield Neural Network Approach for Solving Examination Timetabling Problems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255266.

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The Examination Timetabling Problem (ETP) is the problem of scheduling a number of exams during a set time period so that no students are required to sit two exams simultaneously. Despite the complexity of the problem, universities all over the world solve ETPs several times each year. Two known methods for solving ETPs is using either heuristics or Hopfield Neural Networks (HNN). This thesis compares the performance of a heuristic algorithm implemented with Local Search, Simulated Annealing and Tabu Search to the performance of a HNN algorithm. Both algorithms were executed on ten different ETPs reduced to Graph Colouring Problems (GCP). The results show that the heuristic algorithm always generated more satisfactory solutions to the ETPs than the HNN. The HNN was, however, implemented as software in this thesis. It is intended to be implemented as hardware and if this method were to have been used instead the HNN algorithm might have produced other results. At this stage the heuristic algorithm is more suitable than the HNN algorithm for solving ETPs.
Schemaläggningsproblem för examinationer (ETP) syftar på problemet att schemalägga ett antal examinationer under ett bestämt tidsintervall så att ingen student behöver närvara på flera examinationer samtidigt. Det är ett komplext problem som universitet världen över behöver lösa flera gånger per år. Två kända metoder för att lösa ETPs är användning av antingen heuristiker eller Hopfield Neural Networks (HNN). Den här uppsatsen jämför prestandan av en heuristik implementerad med Lokal Sökning, Simulerad Härdning och Tabusökning med presentandan av en HNN-algoritm då båda metoderna exekveras på tio ETPs reducerade till Graffärgningsproblem (GCP). Resultaten visar att heuristiken alltid genererade mer tillfredsställande lösningar till schemaläggningsproblemen än HNN-algoritmen gjorde. HNN-algoritmen, som egentligen bör implementeras som hårdvara, implementerades dock som mjukvara i den här avhandlingen. Hade den implementerats som hårdvara istället hade kanske andra resultat producerats. För tillfället lämpar sig heuristiken bättre än HNN-algoritmen för att lösa ETPs.

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Piasko, Jacques Marc. "Neural networks : a mathematical analysis of the Hopfield model and a model for the neural integrator of the oculomotor system /." Zürich, 1989. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=8914.

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Li, Jiakai. "AI-WSN: Adaptive and Intelligent Wireless Sensor Networks." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1341258416.

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Balavoine, Aurèle. "Implementation of the locally competitive algorithm on a field programmable analog array." Thesis, Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/37255.

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Sparse approximation is an important class of optimization problem in signal and image processing applications. This thesis presents an analog solution to this problem, based on the Locally Competitive Algorithm (LCA). A Hopfield-Network-like analog system, operating on sub-threshold currents is proposed as a solution. The results of the circuit components' implementation on the RASP2.8a chip, a Field Programmable Analog Array, are presented.

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Pinheiro, Rafael Fernandes. "O problema de Lurie e aplicações às redes neurais." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/45/45132/tde-25042015-223201/.

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Neste trabalho apresentamos um assunto que tem contribuído em diversas áreas, o conhecido Problemas de Lurie. Para exemplificar sua aplicabilidade estudamos a Rede Neural de Hopfield e a relacionamos com o problema. Alguns teoremas são apresentados e um dos resultados do Problema de Lurie é aplicado ao modelo de Hopfield.
In the present work we show some properties of the so called Luries type equation. We treat particularly the stability conditions problem, and show how this theory is applied in a Hopfield neural network.

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Zedníček, Petr. "Síťový prvek s pokročilým řízením." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218333.

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The diploma thesis deal with finding and testing neural networks, whose characteristics and parameters suitable for the active management of network element. Solves optimization task priority switching of data units from input to output. Work is focused largely on the use of Hopfield and Kohonen networks and their optimization. Result of this work are two models. The first theory is solved in Matlab, where each comparing the theoretical results of neural networks. The second model is a realistic model of the active element designed in Simulink

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González, Marek. "Fuzzy neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234941.

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This thesis focuses on fuzzy neural networks. The combination of the fuzzy logic and artificial neural networks leads to the development of more robust systems. These systems are used in various field of the research, such as artificial intelligence, machine learning and control theory. First, we provide a quick overview of underlying neural networks and fuzzy systems to explain fundamental ideas that form the basis of the fields, and follow with the introduction of the fuzzy neural network theory, classification and application. Then we describe a design and a realization of the fuzzy associative memory, as an example of these systems. Finally, we benchmark the realization using the pattern recognition and control tasks. The results are evaluated and compared against existing systems.

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Ould, Mohamed Abdallahi Lémine. "Estimation des paramètres d'un modèle d'activité neuronale et applications de la théorie du champ moyen." Université Joseph Fourier (Grenoble), 2001. http://www.theses.fr/2001GRE10075.

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Ce travail porte sur quelques applications de la théorie du champ moyen en statistique. Dans le chapitre 1 nous nous intéressons à la modélisation de l'activité neuronale. Nous introduisons un modèle qui décrit cette activité à l'aide d'une dynamique markovienne. Le modèle introduit s'inscrit dans le formalisme des systèmes de particules. L'approche utilisée provient de la mécanique statistique et s'inspire fortement de la théorie du champ moyen. Dans le chapitre 2, nous nous intéressons à l'estimation des paramètres du modèle à partir d'observations de l'état stationnaire. Nous proposons différentes méthodes d'estimation. Nous discutons les différentes méthodes et nous les validons par simulation. Nous appliquons ces méthodes à des données réelles recueillies par la méthode de l'enregistrement optique sur le cortex auditif. Dans le chapitre 3, nous construisons un test sur les fluctuations dans le modèle de Hopfield basé sur des théorèmes de limite centrale sur les alignements. Nous montrons que ce test améliore significativement la qualité du rappel dans certains cas

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Štafa, Václav. "Modelování síťového prvku pomocí logického pole." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-375491.

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This Master’s Thesis includes introduction the field programmable logic and their NetFPGA platform developed in the context of its use for routing using neural networks. Current routing protocols and routing methods. Furthermore, the issue of neural networks with a focus on the Hopfield network for data network routing.

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Calabuig, Soler Daniel. "Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks." Doctoral thesis, Universitat Politècnica de València, 2010. http://hdl.handle.net/10251/7348.

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Hoy en día existen varias tecnologías que coexisten en una misma zona formando un sistema heterogéneo. Además, este hecho se espera que se vuelva más acentuado con todas las nuevas tecnologías que se están estandarizando actualmente. Hasta ahora, generalmente son los usuarios los que eligen la tecnología a la que se van a conectar, ya sea configurando sus terminales o usando terminales distintos. Sin embargo, esta solución es incapaz de aprovechar al máximo todos los recursos. Para ello es necesario un nuevo conjunto de estrategias. Estas estrategias deben gestionar los recursos radioeléctricos conjuntamente y asegurar la satisfacción de la calidad de servicio de los usuarios. Siguiendo esta idea, esta Tesis propone dos nuevos algoritmos. El primero es un algoritmo de asignación dinámica de recusos conjunto (JDRA) capaz de asignar recursos a usuarios y de distribuir usuarios entre tecnologías al mismo tiempo. El algoritmo está formulado en términos de un problema de optimización multi-objetivo que se resuelve usando redes neuronales de Hopfield (HNNs). Las HNNs son interesantes ya que se supone que pueden alcanzar soluciones sub-óptimas en cortos periodos de tiempo. Sin embargo, implementaciones reales de las HNNs en ordenadores pierden esta rápida respuesta. Por ello, en esta Tesis se analizan las causas y se estudian posibles mejoras. El segundo algoritmo es un algoritmo de control de admisión conjunto (JCAC) que admite y rechaza usuarios teniendo en cuenta todas las tecnologías al mismo tiempo. La principal diferencia con otros algorimos propuestos es que éstos últimos toman las dicisiones de admisión en cada tecnología por separado. Por ello, se necesita de algún mecanismo para seleccionar la tecnología a la que los usuarios se van a conectar. Por el contrario, la técnica propuesta en esta Tesis es capaz de tomar decisiones en todo el sistema heterogéneo. Por lo tanto, los usuarios no se enlazan con ninguna tecnología antes de ser admitidos.
Calabuig Soler, D. (2010). Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7348
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Šilhan, Petr. "Simulace řízení síťového prvku s neuronovou sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217528.

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The thesis deals with the use of neuronal networks for the control of telecommunication network elements. The aim of the thesis is to create a simulation model of network element with switching array with central memory, in which the optimization control switching array is solved by means of the Hopfield neural network. All source code is created in integrated environment MATLAB with the use of Neural Network Toolbox.

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Pokorný, Petr. "Návrh síťového prvku pomocí neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2008. http://www.nusl.cz/ntk/nusl-228226.

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The diploma thesis deal with a priority network switch whose model was made in programming environment Matlab - Simulink. Problem of optimal switching is solved by Hopfield’s artificial neural network. Produce of the diploma thesis is a model of packet switch and time-severity comparison of optimalization problem solved with or without artificial neural network. The thesis was developed in research project MSM 0021630529 Intelligent Systems in Automation.

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Torzynski, Marc. "Reseaux de neurones formels : proprietes du modele de hopfield, realisations electroniques et optiques." Université Louis Pasteur (Strasbourg) (1971-2008), 1988. http://www.theses.fr/1988STR13230.

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On etudie un modele neuro-mimetique de traitement d'information, susceptible d'etre utilise comme une memoire associative. En premiere partie, on etudie les proprietes du modele et l'on quantifie la capacite memoire. Par simulation numerique, la tolerance du reseau a ses propres deficiences et ses facultes d'associativite. On montre theoriquement plusieurs points relatifs a l'information a l'aide d'une approche probabiliste du modele. En seconde partie, on etudie les possibilites d'implantation du modele

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Lýsek, Jiří. "Optimalizace síťového přepínače pomocí neuronové sítě." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228699.

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This thesis deals with the problem of priority network switch, the model of which was developed in the C++ language. The traffic optimization task is solved by the use of several artificial neural networks, which are described, compared to each other and then evaluated which of them is more suitable for this task. The result of this work is a model of network switch and a comparison of computational time complexity of solving the optimization problem using the artificial neural network. The thesis was developed in research project MSM 0021630529 Intelligent Systems in Automation.

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Shapero, Samuel Andre. "Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/51719.

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Sparse approximation is a Bayesian inference program with a wide number of signal processing applications, such as Compressed Sensing recovery used in medical imaging. Previous sparse coding implementations relied on digital algorithms whose power consumption and performance scale poorly with problem size, rendering them unsuitable for portable applications, and a bottleneck in high speed applications. A novel analog architecture, implementing the Locally Competitive Algorithm (LCA), was designed and programmed onto a Field Programmable Analog Arrays (FPAAs), using floating gate transistors to set the analog parameters. A network of 6 coefficients was demonstrated to converge to similar values as a digital sparse approximation algorithm, but with better power and performance scaling. A rate encoded spiking algorithm was then developed, which was shown to converge to similar values as the LCA. A second novel architecture was designed and programmed on an FPAA implementing the spiking version of the LCA with integrate and fire neurons. A network of 18 neurons converged on similar values as a digital sparse approximation algorithm, with even better performance and power efficiency than the non-spiking network. Novel algorithms were created to increase floating gate programming speed by more than two orders of magnitude, and reduce programming error from device mismatch. A new FPAA chip was designed and tested which allowed for rapid interfacing and additional improvements in accuracy. Finally, a neuromorphic chip was designed, containing 400 integrate and fire neurons, and capable of converging on a sparse approximation solution in 10 microseconds, over 1000 times faster than the best digital solution.

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Šulák, Michal. "Aplikace neuronových sítí v telekomunikacích." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217313.

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This Master’s Thesis consists of description of current routing protocols and routers, basic principles of neural networks and their interpretation in connection with the use for routing in data networks and telecommunications networks. In the thesis I focused on neural networks, which use energetic functions to find solution stabled states and their use for data routing. I produced the application software to test and find suitable variables for each function. This application counts the shortest path and is able to change variables to reach the best solution of stabled state of neural network. These solutions are compared with other functions that are usually used in nowadays systems for data network routing.

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Fajčík, Martin. "Automatizace verifikace pomocí neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363776.

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The goal of this thesis is to analyze and to find solutions of optimization problems derived from automation of functional verification of hardware using artificial neural networks. Verification of any integrated circuit (so called Design Under Verification, DUV) using technique called coverage-driven verification and universal verification methodology (UVM) is carried out by sending stimuli inputs into DUV. The verification environment continuously monitors percentual coverage of DUV functionality given by the specification. In current context, coverage stands for measurable property of DUV, like count of verified arithemtic operations or count of executed lines of code. Based on the final coverage, it is possible to determine whether the coverage of DUV is high enough to declare DUV as verified. Otherwise, the input stimuli set needs to change in order to achieve higher coverage. Current trend is to generate this set by technique called constrained-random stimulus generation. We will practice this technique by using pseudorandom program generator (PNG). In this paper, we propose multiple solutions for following two optimization problems. First problem is ongoing modification of PNG constraints in such a way that the DUV can be verified by generated stimuli as quickly as possible. Second one is the problem of seeking the smallest set of stimuli such that this set verifies DUV. The qualities of the proposed solutions are verified on 32-bit application-specific instruction set processors (ASIPs) called Codasip uRISC and Codix Cobalt.

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Novák, David. "Optimalizace přenosu hlasu v komunikačních sítích." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218340.

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This master’s thesis deals abou the transmission of voice in communications networks. The theoretical part describes criteria for optimizing voice, such as quality of service, type of service, level of service, service type, and mean opinion score. Next I describe the Internet Protocol, comparing IPv4 and IPv6, VoIP, including security, protocols and parameters necessary for transmission. Other part is about neural networks. There are basically described the neural network, Hopfield neural network and Kohenen neural network. The research is based on a comparison of the network without ensuring the quality of service and with ensuring quality of service. Then, there are compared two types of switches. Classical switch-controlled sequentially, and switch controlled by neural networks. The overall simulation program is implemented in Opnet Modeler. The conclusion deals with the creation of laboratory tasks in this program to compare the different systems of ensuring quality of service.

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Marashdeh, Qussai Mohammad. "Advances in electrical capacitance tomography." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1148591259.

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Stískal, Břetislav. "Návrh algoritmů pro neuronové sítě řídicí síťový prvek." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217527.

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This diploma thesis is devided into theoretic and practice parts. Theoretic part contains basic information about history and development of Artificial Neural Networks (ANN) from last century till present. Prove of the theoretic section is discussed in the practice part, for example learning, training each types of topology of artificial neural networks on some specifics works. Simulation of this networks and then describing results. Aim of thesis is simulation of the active networks element controlling by artificial neural networks. It means learning, training and simulation of designed neural network. This section contains algorithm of ports switching by address with Hopfield's networks, which used solution of typical Trade Salesman Problem (TSP). Next point is to sketch problems with optimalization and their solutions. Hopfield's topology is compared with Recurrent topology of neural networks (Elman's and Layer Recurrent's topology) their main differents, their advantages and disadvantages and supposed their solution of optimalization in controlling of network's switch. From thesis experience is introduced solution with controll function of ANN in active networks elements in the future.

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Wei, Li Qun, and 魏立群. "Solving broadcast scheduling in a multihop radio network by hopfield neural networks." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/88787035701760124787.

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Li, Chun-Hung, and 李俊宏. "Optimal Chiller Loading Using Hopfield Neural Network." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/pm7thb.

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碩士
國立臺北科技大學
冷凍空調工程系所
94
The chiller loading distribution methods include Average Loading (AVL) method, Largrangian Multiplier (LGM) method and Genetic Algorithm (GA) at present. Each of these methods has its own shortcomings. For example, AVL method may be the most popular used method but not the optimal one. Also, even the Lagrangian Multiplier Method（LGM） is able to get the optimal chiller loading distribution (OCLD) when the kW-PLR performance curve is convex function, it can not achieve the optimal solution when convex function and non-convex function exist together in the kW-PLR performance curve. Although the Genetic Algorithm (GA) method could overcome these shortcomings, its programming design is very complicated. It processes include reproduction, crossover, mutation, encoding and decoding etc. On the other hand, the Hopfield method leaves out all of these processes in the GA approach and is able to adjust itself to figure out the optimal value. By using the Hopfield method, it can decrease the tolerance and make the programming much easier. As a result, the Hopfield is used as the main research method to compare with the methods of Average Loading (AVL), Largrangian Multiplier (LGM), and Genetic Algorithm (GA). The main purpose of getting the OCLD is to not only minimize the system power consumption but also meet the HVAC system load and eventually achieve the optimal part load ratio (PLR) of each chiller. The results of the research paper indicates that the Hopfield method has better performance on finding out the most efficient distribution loading than the GA does.

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Dissertations / Theses on the topic 'Hopfield network; Neural networks'

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