Open access peer-reviewed Edited Volume

Recurrent Neural Networks for Temporal Data Processing

The RNNs (Recurrent Neural Networks) are a general case of artificial neural networks where the connections are not feed-forward ones only. In RNNs, connections between units form directed cycles, providing an implicit internal memory. Those RNNs are adapted to problems dealing with signals evolving through time. Their internal memory gives them the ability to naturally take time into account. Valuable approximation results have been obtained for dynamical systems.

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Recurrent Neural Networks for Temporal Data ProcessingEdited by Hubert Cardot

Published: February 9th 2011

DOI: 10.5772/631

ISBN: 978-953-307-685-0

Copyright year: 2011

Books open for chapter submissions

9683 Total Chapter Downloads

3 Crossref Citations

16 Web of Science Citations

19 Dimensions Citations

chaptersDownloads

Open access peer-reviewed

1. Double Seasonal Recurrent Neural Networks for Forecasting Short Term Electricity Load Demand in Indonesia

By Sony Sunaryo, Suhartono Suhartono and Alfonsus J. Endharta

2413

Open access peer-reviewed

2. Advanced Methods for Time Series Prediction Using Recurrent Neural Networks

By Romuald Boné and Hubert Cardot

2490

Open access peer-reviewed

3. A New Application of Recurrent Neural Networks for EMG-based Diagnosis of Carpal Tunnel Syndrome

By Konuralp Ilbay, Elif Derya Ubeyli, Gul Ilbay and Faik Budak

1662

Open access peer-reviewed

4. Modeling of Hysteresis in Human Meridian System with Recurrent Neural Networks

By Yonghong Tan, Ruili Dong and Hui Chen

1094

Open access peer-reviewed

5. Toward an Integrative Dynamic Recurrent Neural Network for Sensorimotor Coordination Dynamics

By Cheron G., Duvinage M., Castermans, T. Leurs F., Cebolla A., Bengoetxea A., De Saedeleer C., Petieau M., Hoellinger T., Seetharaman K., Draye JP. and Dan B

1139

Open access peer-reviewed

6. Compact Internal Representation as a Functional Basis for Protocognitive Exploration of Dynamic Environments

By Valeri A. Makarov and José Antonio Villacorta-Atienza

885

Edited Volume and chapters are indexed in

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  • Google Scholar
  • AZ ebsco
  • Base
  • CNKI
  • IET Inspec

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