Results 41 to 50 of about 3,974,691 (210)
Applications of recurrent neural networks in batch reactors. Part I: NARMA modelling of the dynamic behaviour of the heat transfer fluid [PDF]
, 1997 This paper is focused on the development of nonlinear models, using artificial neural networks, able to provide appropriate predictions when acting as process simulators. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical
J.M. Zaldı́var +3 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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A delay-dependent LMI approach to dynamics analysis of discrete-time recurrent neural networks with time-varying delays [PDF]
, 2007 This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this Letter, the analysis problem for the existence and stability of periodic solutions is investigated for
Zidong Wang +3 more
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Bayesian Recurrent Neural Networks
CoRR, 2017 In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks. Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by
Meire Fortunato +2 more
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Restricted Recurrent Neural Networks [PDF]
2019 IEEE International Conference on Big Data (Big Data), 2019 Recurrent Neural Network (RNN) and its variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), have become standard building blocks for learning online data of sequential nature in many research areas, including natural language processing and speech data analysis.
Enmao Diao, Jie Ding 0002, Vahid Tarokh
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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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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
doaj
ETRI Journal, 2017 Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection.
Hyoung‐Gook Kim, Jin Young Kim
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Discrete-time recurrent neural networks with time-varying delays: Exponential stability analysis [PDF]
, 2007 This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier LtdThis Letter is concerned with the analysis problem of exponential stability for a class of discrete-time ...
Liu, Y, Liu, X, Wang, Z, Serrano, A
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