Results 241 to 250 of about 92,175 (266)

Recurrent Neural Networks Are Universal Approximators

International Journal of Neural Systems, 2006
Recurrent Neural Networks (RNN) have been developed for a better understanding and analysis of open dynamical systems. Still the question often arises if RNN are able to map every open dynamical system, which would be desirable for a broad spectrum of applications.
Anton Maximilian Schäfer, Hans-Georg Zimmermann   +1 more
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Recurrent neural networks for syllabification

Speech Communication, 1993
Abstract An important procedure in many prosodic analysis systems is locating syllables. The location of syllables is used in the identification of stress and of pitch accents, which in turn form the basis for the analysis of rhythm and intonation. This paper presents a novel syllabification system utilising recurrent neural networks which operates ...
openaire   +1 more source

Randomized Recurrent Neural Networks. [PDF]

, 2018
Neural Networks (NNs) with random weights represent nowadays a topic of consolidated use in the Machine Learning research community. In this contribution we focus in particular on recurrent NN models, which in a randomized setting represent a case of particular interest per se, entailing a number of intriguing research challenges primarily related to ...
Claudio Gallicchio, Alessio Micheli, Peter Tino   +2 more
openaire   +1 more source

Recurrent Neural Network Architectures

2017
In this chapter, we present three different recurrent neural network architectures that we employ for the prediction of real-valued time series. All the models reviewed in this chapter can be trained through the previously discussed backpropagation through time procedure.
Bianchi, Filippo Maria, Maiorino, Enrico, Kampffmeyer, Michael C., Rizzi, Antonello, Jenssen, Robert   +4 more
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Selective Recurrent Neural Network

Neural Processing Letters, 2012
It is known that recurrent neural networks may have difficulties remembering data over long time lags. To overcome this problem, we propose an extended architecture of recurrent neural networks, which is able to deal with long time lags between relevant input signals.
openaire   +1 more source

A Lyapunov-stability-based context-layered recurrent pi-sigma neural network for the identification of nonlinear systems

Applied Soft Computing Journal, 2022
Rajesh Kumar
exaly  

Adaptive Global Sliding-Mode Control for Dynamic Systems Using Double Hidden Layer Recurrent Neural Network Structure

IEEE Transactions on Neural Networks and Learning Systems, 2020
Yundi Chu, Juntao Fei, Shixi Hou
exaly  

A New Varying-Parameter Recurrent Neural-Network for Online Solution of Time-Varying Sylvester Equation

IEEE Transactions on Cybernetics, 2018
Zhijun Zhang, Lunan Zheng, Jian Weng
exaly  

Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery

IEEE Transactions on Geoscience and Remote Sensing, 2019
Lichao Mou, Lorenzo Bruzzone, Xiao Xiang Zhu   +2 more
exaly  

Spatial–Temporal Recurrent Neural Network for Emotion Recognition

IEEE Transactions on Cybernetics, 2019
Tong Zhang, Wenming Zheng, Zhen Cui
exaly