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Littérature scientifique sur le sujet « Hopfield Neural Networks »

Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 16 juin 2025

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Articles de revues sur le sujet "Hopfield Neural Networks"

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Kobayashi, Masaki. "Storage Capacities of Twin-Multistate Quaternion Hopfield Neural Networks." Computational Intelligence and Neuroscience 2018 (November 1, 2018): 1–5. http://dx.doi.org/10.1155/2018/1275290.

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A twin-multistate quaternion Hopfield neural network (TMQHNN) is a multistate Hopfield model and can store multilevel information, such as image data. Storage capacity is an important problem of Hopfield neural networks. Jankowski et al. approximated the crosstalk terms of complex-valued Hopfield neural networks (CHNNs) by the 2-dimensional normal distributions and evaluated their storage capacities. In this work, we evaluate the storage capacities of TMQHNNs based on their idea.
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Wilson, Robert C. "Parallel Hopfield Networks." Neural Computation 21, no. 3 (2009): 831–50. http://dx.doi.org/10.1162/neco.2008.03-07-496.

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We introduce a novel type of neural network, termed the parallel Hopfield network, that can simultaneously effect the dynamics of many different, independent Hopfield networks in parallel in the same piece of neural hardware. Numerically we find that under certain conditions, each Hopfield subnetwork has a finite memory capacity approaching that of the equivalent isolated attractor network, while a simple signal-to-noise analysis sheds qualitative, and some quantitative, insight into the workings (and failures) of the system.
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Kobayashi, Masaki. "Bicomplex Projection Rule for Complex-Valued Hopfield Neural Networks." Neural Computation 32, no. 11 (2020): 2237–48. http://dx.doi.org/10.1162/neco_a_01320.

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A complex-valued Hopfield neural network (CHNN) with a multistate activation function is a multistate model of neural associative memory. The weight parameters need a lot of memory resources. Twin-multistate activation functions were introduced to quaternion- and bicomplex-valued Hopfield neural networks. Since their architectures are much more complicated than that of CHNN, the architecture should be simplified. In this work, the number of weight parameters is reduced by bicomplex projection rule for CHNNs, which is given by the decomposition of bicomplex-valued Hopfield neural networks. Computer simulations support that the noise tolerance of CHNN with a bicomplex projection rule is equal to or even better than that of quaternion- and bicomplex-valued Hopfield neural networks. By computer simulations, we find that the projection rule for hyperbolic-valued Hopfield neural networks in synchronous mode maintains a high noise tolerance.
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Ismailov, Mirxalil, Davron Ziyadullaev, Dilnoz Muhamediyeva, Rano Gazieva, Aksulu Dzholdasbaeva, and Sharofiddin Aynaqulov. "Intelligent algorithms of construction of public transport routes." E3S Web of Conferences 365 (2023): 01002. http://dx.doi.org/10.1051/e3sconf/202336501002.

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Today, in public transport planning systems, it is relevant to a search for a possible route with a minimum time. The aim of the work is the development of intelligent algorithms for constructing public transport routes, the development of programs, and the conduct of a computational experiment. Research methods are the theory of neural networks. The paper considers Hopfield neural networks and proposed recurrent neural networks. However, in Hopfield neural networks, the chances of solving this optimization problem decrease as the matrix size increases. A recurrent neural network is proposed, represented by a differential equation to solve this problem. As a result, the number of iterative computations can be reduced by n2 times than in the Hopfield network.
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Feild, William B., and Jainendra K. Navlakha. "On Hopfield neural networks." Neural Networks 1 (January 1988): 21. http://dx.doi.org/10.1016/0893-6080(88)90063-9.

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Kobayashi, M. "Hyperbolic Hopfield Neural Networks." IEEE Transactions on Neural Networks and Learning Systems 24, no. 2 (2013): 335–41. http://dx.doi.org/10.1109/tnnls.2012.2230450.

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Hu, Shigeng, Xiaoxin Liao, and Xuerong Mao. "Stochastic Hopfield neural networks." Journal of Physics A: Mathematical and General 36, no. 9 (2003): 2235–49. http://dx.doi.org/10.1088/0305-4470/36/9/303.

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Lai, Xiao Feng, Xing Yin, and Hai Yang Zou. "Stability Criterion of Discrete Hopfield Neural Networks with Multiple Delays in Parallel Mode: Linear Matrix Inequality." Advanced Materials Research 225-226 (April 2011): 1270–73. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.1270.

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In this paper, Discrete Hopfield Neural Networks with Multple Delays is introduced.And a stability criterion of Discrete Hopfield Neural Networks with Multple Delays is studied bythe linear matrix inequality. It provides a theory basis for the application of Discrete Hopfield Neural Networks with Multple Delays.
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Fan, Tongke. "Research on Optimal CDMA Multiuser Detection Based on Stochastic Hopfield Neural Network." Recent Patents on Computer Science 12, no. 3 (2019): 233–40. http://dx.doi.org/10.2174/2213275912666181210103742.

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Background: Most of the common multi-user detection techniques have the shortcomings of large computation and slow operation. For Hopfield neural networks, there are some problems such as high-speed searching ability and parallel processing, but there are local convergence problems. Objective: The stochastic Hopfield neural network avoids local convergence by introducing noise into the state variables and then achieves the optimal detection. Methods: Based on the study of CDMA communication model, this paper presents and models the problem of multi-user detection. Then a new stochastic Hopfield neural network is obtained by introducing a stochastic disturbance into the traditional Hopfield neural network. Finally, the problem of CDMA multi-user detection is simulated. Conclusion: The results show that the introduction of stochastic disturbance into Hopfield neural network can help the neural network to jump out of the local minimum, thus achieving the minimum and improving the performance of the neural network.
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YAN, JUN-JUH, TEH-LU LIAO, JUI-SHENG LIN, and CHAO-JUNG CHENG. "SYNCHRONIZATION CONTROL OF NEURAL NETWORKS SUBJECT TO TIME-VARYING DELAYS AND INPUT NONLINEARITY." International Journal of Bifurcation and Chaos 16, no. 12 (2006): 3643–54. http://dx.doi.org/10.1142/s0218127406017038.

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This paper investigates the synchronization problem for a particular class of neural networks subject to time-varying delays and input nonlinearity. Using the variable structure control technique, a memoryless decentralized control law is established which guarantees exponential synchronization even when input nonlinearity is present. The proposed controller is suitable for application in delayed cellular neural networks and Hopfield neural networks with no restriction on the derivative of the time-varying delays. A two-dimensional cellular neural network and a four-dimensional Hopfield neural network, both with time-varying delays, are presented as illustrative examples to demonstrate the effectiveness of the proposed synchronization scheme.
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Thèses sur le sujet "Hopfield Neural Networks"

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

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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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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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Livres sur le sujet "Hopfield Neural Networks"

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P, Coughlin James. Neural computation in Hopfield networks and Boltzmann machines. University of Delaware Press, 1995.

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Keeler, James David. Comparison between sparsely distributed memory and Hopfield-type neural network models. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1986.

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Rojas, Raúl. Neural networks: A systematic introduction. Springer-Verlag, 1996.

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Center, Ames Research, ed. Simulation tests of the optimization method of Hopfield and Tank using neural networks. National Aeronautics and Space Administration, Ames Research Center, 1989.

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Dayhoff, Judith E. Neural network architectures: An introduction. Van Nostrand Reinhold, 1990.

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Ally, Afshan. A Hopfield neural network decoder for convolutional codes. National Library of Canada = Bibliothèque nationale du Canada, 1991.

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Collier, N. H. English-Japanese lexical transfer using a hopfield neural network. UMIST, 1996.

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Cierniak, Robert. Nowe algorytmy rekonstrukcji obrazu z projekcji z zastosowaniem sieci neuronowych typu Hopfielda. Wydawn. Politechniki Częstochowskiej, 2006.

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P, Coughlin James, and Robert H. Baran. Neural Computation in Hopfield Networks and Boltzmann Machines. Rowman & Littlefield Publishers, Incorporated, 1995.

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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. 1989.

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Chapitres de livres sur le sujet "Hopfield Neural Networks"

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Rojas, Raúl. "The Hopfield Model." In Neural Networks. Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-642-61068-4_13.

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Davalo, Eric, and Patrick Naïm. "The Hopfield Model." In Neural Networks. Macmillan Education UK, 1991. http://dx.doi.org/10.1007/978-1-349-12312-4_4.

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Ansari, Nirwan, and Edwin Hou. "Hopfield Neural Networks." In Computational Intelligence for Optimization. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6331-0_3.

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da Silva, Ivan Nunes, Danilo Hernane Spatti, Rogerio Andrade Flauzino, Luisa Helena Bartocci Liboni, and Silas Franco dos Reis Alves. "Recurrent Hopfield Networks." In Artificial Neural Networks. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43162-8_7.

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Akhmet, Marat, Madina Tleubergenova, Akylbek Zhamanshin, and Zakhira Nugayeva. "Hopfield-Type Neural Networks." In Artificial Neural Networks. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68966-6_3.

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Katsaggelos, Aggelos K. "Hopfield-Type Neural Networks." In Digital Image Restoration. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-58216-5_3.

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Yi, Zhang, and K. K. Tan. "Hopfield Recurrent Neural Networks." In Network Theory and Applications. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4757-3819-3_2.

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Müller, Berndt, Joachim Reinhardt, and Michael T. Strickland. "The Hopfield Network for p/N → 0." In Neural Networks. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57760-4_18.

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Müller, Berndt, Joachim Reinhardt, and Michael T. Strickland. "The Hopfield Network for Finite p/N." In Neural Networks. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-57760-4_19.

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Müller, Berndt, and Joachim Reinhardt. "The Hopfield Network for p/N → o." In Neural Networks. Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-97239-3_16.

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Actes de conférences sur le sujet "Hopfield Neural Networks"

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Vallejo, Jose Refugio, and Eduardo Bayro-Corrochano. "Clifford Hopfield Neural Networks." In 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong). IEEE, 2008. http://dx.doi.org/10.1109/ijcnn.2008.4634314.

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Atkins. "Sorting by Hopfield net." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118679.

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Lee Giles, C., and Mark W. Goudreau. "A Neural Network Router for Optical Interconnection Networks." In Optical Computing. Optica Publishing Group, 1993. http://dx.doi.org/10.1364/optcomp.1993.otua.8.

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Abe. "Theories on the Hopfield neural networks." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118633.

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Li, John X., and Francis T. S. Yu. "Karhunen-Loeve training process for optical neural networks." In OSA Annual Meeting. Optica Publishing Group, 1990. http://dx.doi.org/10.1364/oam.1990.mn4.

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The Karhunen-Loeve (K-L) transform has been applied to the training of optical neural networks. Because orthogonal images are used in the training, the K-L training process improves the Hopfield-model capacity of a neural network. Moreover, pattern recognition based on the structure of images and the Hamming distance is achieved for neural networks. For the design of optical neural networks, the relation between the minimum focal length of the two-dimensional optical neural networks and the maximum number of neurons is derived.
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"PARALLEL EVALUATION OF HOPFIELD NEURAL NETWORKS." In International Conference on Neural Computation Theory and Applications. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003682902480253.

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Murthy, Garimella Rama, and Moncef Gabbouj. "On the design of Hopfield Neural Networks: Synthesis of hopfield type associative memories." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280299.

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Kamgar-Parsi and Kamgar-Parsi. "On problem solving with Hopfield neural networks." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118363.

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Kojic, Nenad S. "Implementations of Hopfield neural network in communication networks." In 2013 21st Telecommunications Forum Telfor (TELFOR). IEEE, 2013. http://dx.doi.org/10.1109/telfor.2013.6716253.

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Ueda, Yasuhiro, Masakazu Kawahara, Takashi Inoue, Yoko Uwate, and Yoshifumi Nishio. "Space-varying cellular neural networks designed by Hopfield neural network." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596510.

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Rapports d'organisations sur le sujet "Hopfield Neural Networks"

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Wieselthier, Jeffrey E., and Craig M. Barnhart. The Application of Hopfield Neural Network Techniques to Problems of Routing and Scheduling in Packet Radio Networks. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada229039.

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Valdez, Luis, Miltos Alamaniotis, and Alexander Heifetz. Detection of Isotopes in Urban Source Search Gamma Spectra Using Hopfield Neural Network. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2319221.

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Valdez, Luis, and Alexander Heifetz. Preliminary Assessment of Qiskit Quantum Simulator Capabilities for Development of Quantum Hopfield Neural Network for Anomaly Detection Applications (Q4 Report). Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1832153.

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Valdez, Luis, Miltos Alamaniotis, and Alexander Heifetz. Anomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover’s Algorithm on a Quantum Computing Simulator. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1894587.

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Valdez, Luis, and Alexander Heifetz. Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1827413.

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Valdez, Luis, and Alexander Heifetz. Development of Hopfield Artificial Neural Network for Anomaly Detection in Environmental Gamma Radiation Background - Consortium on Nuclear Security Technologies (CONNECT) Q2 Report. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1827640.

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