Recurrent neural networks - Open Access .click

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Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation. [PDF]

, 2008
In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear state-space form is used to predict the future ...
Cao, Yi, Al Seyab, Rihab Khalid Shakir
core +1 more source

Quasi-Recurrent Neural Networks

CoRR, 2016
Submitted to conference track at ICLR ...
James Bradbury 0002 +3 more
openaire +3 more sources

Estimation of applicability of modern neural network methods for preventing cyberthreats to self-organizing network infrastructures of digital economy platformsa,b

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, Krundyshev Vasiliy, Zubkov Evgeny +2 more
doaj +1 more source

From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing

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, Gabriel Omar Masi, Luis Argerich +2 more
doaj +1 more source

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
doaj +1 more source

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, Charles Blundell, Oriol Vinyals +2 more
openaire +2 more sources

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

Acoustic Event Detection in Multichannel Audio Using Gated Recurrent Neural Networks with High‐Resolution Spectral Features

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
doaj +1 more source

Quaternion Recurrent Neural Networks

CoRR, 2018
ICLR Update - Full ...
Parcollet, Titouan +6 more
openaire +4 more sources

A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks

Sensors, 2023
This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to
Francisco Rau +6 more
doaj +1 more source

deep learning
neural networks
recurrent neural network

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convolutional neural network