Results 31 to 40 of about 354,413 (309)
Recognizing recurrent neural networks (rRNN): Bayesian inference for recurrent neural networks [PDF]
Biological Cybernetics, 2012 Recurrent neural networks (RNNs) are widely used in computational neuroscience and machine learning applications. In an RNN, each neuron computes its output as a nonlinear function of its integrated input. While the importance of RNNs, especially as models of brain processing, is undisputed, it is also widely acknowledged that the computations in ...
Bitzer, S., Kiebel, S.
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IEEE Access, 2020 In this paper, ultrasound imaging of benign and malignant thyroid nodules to predict the depth of the learning algorithm, built on circulation volume product thyroid ultrasound image neural network forecasting model.
Yinghui Lu, Yi Yang, Wan Chen
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Компютерні системи та інформаційні технології, 2023 The connectionist-metaheuristic approach solved the urgent task of using new approaches to analyze the foreign direct investment and macroeconomic indicators that affect the volume of their attraction to a particular country in the world economy.
Maryna LESHCHENKO +3 more
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Equivalence of Equilibrium Propagation and Recurrent Backpropagation
, 2018 Recurrent Backpropagation and Equilibrium Propagation are supervised learning algorithms for fixed point recurrent neural networks which differ in their second phase.
Bengio, Yoshua, Scellier, Benjamin
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PNNARMA model: an alternative to phenomenological models in chemical reactors [PDF]
, 2001 This paper is focused on the development of non-linear neural models able to provide appropriate predictions when acting as process simulators. Parallel identification models can be used for this purpose.
Galván, Inés M. +2 more
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Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification
IEEE Access, 2021 A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design ...
Kaisheng Liao +4 more
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, 2018 Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memory and input-output functionalities indissolubly entangled. We introduce a
Bacciu, Davide +2 more
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Feed-forward chains of recurrent attractor neural networks with finite dilution near saturation [PDF]
, 2005 A stationary state replica analysis for a dual neural network model that interpolates between a fully recurrent symmetric attractor network and a strictly feed-forward layered network, studied by Coolen and Viana, is extended in this work to account for ...
Metz, F. L., Theumann, W. K.
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An Improved Recurrent Neural Network for Industrial IoT Botnet Attack Detection
Журнал інженерних наукThis research aims to improve the Industrial Internet of Things (IIoT) security, which fosters technological confidence and promotes expansion. The IIoT is mainly used in manufacturing, oil, and gas to avoid botnet attacks.
G. Suneetha, D.H. Priya
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Evaluation of the Performance of Feedforward and Recurrent Neural Networks in Active Cancellation of Sound Noise [PDF]
Journal of Intelligent Procedures in Electrical Technology, 2012 Active noise control is based on the destructive interference between the primary noise and generated noise from the secondary source. An antinoise of equal amplitude and opposite phase is generated and combined with the primary noise.
Mehrshad Salmasi, Homayoun Mahdavi-Nasab
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