Results 21 to 30 of about 75,210 (267)
Engineering Proceedings, 2023 Artificial neural networks with different structures are used for identification of complex dynamic plant with distributed parameters. The plant is a high-temperature plasma in the spherical Globus-M2 tokamak.
Valerii I. Kruzhkov +2 more
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IEEE Access, 2022 In this paper, a class of Clifford-valued neutral-type recurrent neural networks with $D$ operator is explored. By using non-decomposition method and the Banach fixed point theorem, we obtain several sufficient conditions for the existence of anti ...
Jin Gao, Lihua Dai
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Bidirectional recurrent neural networks [PDF]
IEEE Transactions on Signal Processing, 1997 In the first part of this paper, a regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN). The BRNN can be trained without the limitation of using input information just up to a preset future frame. This is accomplished by training it simultaneously in positive and negative time direction.
Mike Schuster, Kuldip K. Paliwal
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On the quantization of recurrent neural networks
CoRR, 2021 Integer quantization of neural networks can be defined as the approximation of the high precision computation of the canonical neural network formulation, using reduced integer precision. It plays a significant role in the efficient deployment and execution of machine learning (ML) systems, reducing memory consumption and leveraging typically faster ...
Jian Li, Raziel Alvarez
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Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition
Frontiers in Psychology, 2017 Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in
Courtney J. Spoerer +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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SHS Web of Conferences, 2018 The problems of applying neural network methods for solving problems of preventing cyberthreats to flexible self-organizing network infrastructures of digital economy platforms: vehicle adhoc networks, wireless sensor networks, industrial IoT, “smart ...
Kalinin Maxim +2 more
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Inteligencia Artificial, 2018 In recent studies Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, Data Compression is also based on prediction.
Juan Andres Laura +2 more
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Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling [PDF]
Hydrology and Earth System Sciences, 2013 Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall–runoff models.
N. J. de Vos
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Interpretation of recurrent neural networks [PDF]
Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop, 2002 This paper addresses techniques for interpretation and characterization of trained recurrent nets for time series problems. In particular, we focus on assessment of effective memory and suggest an operational definition of memory. Further we discuss the evaluation of learning curves.
Pedersen, Morten With +1 more
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