Showing 41 - 59 results of 59 for search '"Hopfield Network"', query time: 0.09s Refine Results
  1. 41

    IoT-Oriented Design of an Associative Memory Based on Impulsive Hopfield Neural Network with Rate Coding of LIF Oscillators by Petr Boriskov

    Published 2020-09-01
    “…This study presents a design of new associative memory in the form of impulsive Hopfield network based on leaky integrated-and-fire (LIF) RC oscillators with frequency control and hybrid analog–digital coding. …”
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    Article
  2. 42

    Learning algorithms for oscillatory neural networks as associative memory for pattern recognition by Manuel Jiménez, María J. Avedillo, Bernabé Linares-Barranco, Juan Núñez

    Published 2023-11-01
    “…An extensive amount of literature is available about learning in Hopfield networks, with information regarding many different learning algorithms that perform better than the Hebbian rule. …”
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    Article
  3. 43

    Development of a Dynamically Adaptable Routing System for Data Analytics Insights in Logistic Services by Vasileios Tsoukas, Eleni Boumpa, Vasileios Chioktour, Maria Kalafati, Georgios Spathoulas, Athanasios Kakarountas

    Published 2023-04-01
    “…Several algorithms were combined along with a modified Hopfield network to deliver the optimal solution to a multiobjective issue on a platform capable of monitoring the various phases of the process. …”
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    Article
  4. 44

    Intelligent algorithms of construction of public transport routes by Ismailov Mirxalil, Ziyadullaev Davron, Muhamediyeva Dilnoz, Gazieva Rano, Dzholdasbaeva Aksulu, Aynaqulov Sharofiddin

    Published 2023-01-01
    “…As a result, the number of iterative computations can be reduced by n2 times than in the Hopfield network.…”
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    Article
  5. 45

    Effectiveness of Selected Neural Network Structures Based on Axial Flux Analysis in Stator and Rotor Winding Incipient Fault Detection of Inverter-fed Induction Motors by Maciej Skowron, Marcin Wolkiewicz, Teresa Orlowska-Kowalska, Czeslaw T. Kowalski

    Published 2019-06-01
    “…In order to automate the fault detection process, three different structures of neural networks were used: multi-layer perceptron, self-organizing Kohonen network and recursive Hopfield network. Tests were carried out for various levels of stator and rotor failures. …”
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    Article
  6. 46

    Research Progress on Rapid Optimization Design Methods of Metamaterials Based on Intelligent Algorithms by Yuxiang JIA, Jiafu WANG, Wei CHEN, Sai SUI, Ruichao ZHU, Tianshuo QIU, Yongfeng LI, Yajuan HAN, Shaobo QU

    Published 2021-04-01
    “…This paper summarizes the application of several typical intelligent algorithms, including the genetic algorithm, Hopfield network algorithm, and deep learning algorithm in metamaterials design, which include forward designs and an inverse design. …”
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    Article
  7. 47

    Neuromorphic Metamaterials for Mechanosensing and Perceptual Associative Learning by Katherine S. Riley, Subhadeep Koner, Juan C. Osorio, Yongchao Yu, Harith Morgan, Janav P. Udani, Stephen A. Sarles, Andres F. Arrieta

    Published 2022-12-01
    “…Sequentially applied mechanical inputs result in accumulated memristance changes that allow us to physically encode a Hopfield network into the neuromorphic metamaterials. This physical network learns the history of spatially distributed input patterns. …”
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    Article
  8. 48

    Beyond the Maximum Storage Capacity Limit in Hopfield Recurrent Neural Networks by Giorgio Gosti, Viola Folli, Marco Leonetti, Giancarlo Ruocco

    Published 2019-07-01
    “…This thus limits the potential use of this kind of Hopfield network as an associative memory. This paper presents a strategy to overcome this limitation by improving the error correcting characteristics of the Hopfield neural network. …”
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    Article
  9. 49

    The storage capacity of a directed graph and nodewise autonomous, ubiquitous learning by Hui Wei, Fushun Li

    Published 2023-10-01
    “…Experimental results reveal that the proposed adaptive connectivity learning algorithm for directed graphs in this paper possesses the following four features: (1) Demonstrating distributed, self-organizing, and self-adaptive properties, the algorithm achieves global-level functions through local node interactions; (2) Enabling incremental storage and supporting continuous learning capabilities; (3) Displaying stable memory performance, it surpasses the Hopfield network in memory accuracy, capacity, and diversity, as demonstrated in experimental comparisons. …”
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    Article
  10. 50

    Una red neuronal binaria para la identificación de mecanismos isomorfos. // A binary Neural network for identifying isomorphic mechanisms. by G. Galán Marín, J. M. del Castillo Granados

    Published 2002-05-01
    “…</p><p><br />Keywords: isomorphic mechanisms, synthesis of mechanisms, graph isomorphism, binary neural<br />network, Hopfield networks.</p>…”
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    Article
  11. 51

    Mathematical modeling and simulation of stem cell differentiation and reprogramming by Waddington's epigenetic landscape by Guo, Jing

    Published 2018
    “…Here, we proposed a data-driven method, named HopLand, to model the process of stem cell fate determination by using the continuous Hopfield Network (CHN) to map cells in a Waddington’s epigenetic land- scape. …”
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    Thesis
  12. 52

    Stochastic Antiresonance for Systems with Multiplicative Noise and Sector-Type Nonlinearities by Adrian-Mihail Stoica, Isaac Yaesh

    Published 2024-01-01
    “…Such systems arise in a variety of situations such as in engineering applications, in physics, in biology, and in systems with more general nonlinearities, approximated by a wide neural network of a single hidden layer, such as the error equation of Hopfield networks with respect to equilibria or visuo-motor tasks. …”
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    Article
  13. 53

    Phase diagram and storage capacity of sequence storing neural networks by During, A, Coolen, A, Sherrington, D

    Published 1999
    “…The effective retarded self-interaction usually appearing in symmetric models is here found to vanish, which causes a significantly enlarged storage capacity of alpha(c) approximate to 0.269, compared to alpha(c) approximate to 0.139 for Hopfield networks storing static patterns. Our results are tested against extensive computer simulations and excellent agreement is found.…”
    Conference item
  14. 54

    Neural computing with coherent laser networks by Miri Mohammad-Ali, Menon Vinod

    Published 2023-01-01
    “…In addition, the underlying dynamical model discussed here suggests a novel energy-based recurrent neural network that handles continuous data as opposed to Hopfield networks and Boltzmann machines that are intrinsically binary systems.…”
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    Article
  15. 55

    Associative memories via predictive coding by Salvatori, T, Song, Y, Hong, Y, Sha, L, Frieder, S, Xu, Z, Bogacz, R, Lukasiewicz, T

    Published 2021
    “…In an extensive comparison, we show that this new model outperforms in retrieval accuracy and robustness popular associative memory models, such as autoencoders trained via backpropagation, and modern Hopfield networks. In particular, in completing partial data points, our model achieves remarkable results on natural image datasets, such as ImageNet, with a surprisingly high accuracy, even when only a tiny fraction of pixels of the original images is presented. …”
    Conference item
  16. 56

    Concentration or distraction? A synergetic-based attention weights optimization method by Zihao Wang, Haifeng Li, Lin Ma, Feng Jiang

    Published 2023-06-01
    “…Research on modern Hopfield networks indicates that the attention mechanism can also be used in shallow networks. …”
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    Article
  17. 57

    Recurrent predictive coding models for associative memory employing covariance learning. by Mufeng Tang, Tommaso Salvatori, Beren Millidge, Yuhang Song, Thomas Lukasiewicz, Rafal Bogacz

    Published 2023-04-01
    “…This makes the structure of the model inconsistent with the known connectivity of CA3 and classical recurrent models such as Hopfield Networks, which learn the covariance of inputs through their recurrent connections to perform AM. …”
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    Article
  18. 58

    Recurrent predictive coding models for associative memory employing covariance learning by Tang, M, Salvatori, T, Millidge, B, Song, Y, Lukasiewicz, T, Bogacz, R

    Published 2023
    “…This makes the structure of the model inconsistent with the known connectivity of CA3 and classical recurrent models such as Hopfield Networks, which learn the covariance of inputs through their recurrent connections to perform AM. …”
    Journal article
  19. 59

    A novel processor for dynamic evolution of constrained SAT problems: The dynamic evolution variant of the discrete Hopfield neural network satisfiability model by Caicai Feng, Saratha Sathasivam

    Published 2024-01-01
    “…This model adeptly integrates logical rules into Hopfield networks, excelling in locating global minima for traditional SAT problems. …”
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    Article