Recurrent Neural Networks for Temporal Data Processing / / edited by Hubert Cardot and Romuald Boné.
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...
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| Place / Publishing House: | Croatia : : IntechOpen,, 2011. |
| Year of Publication: | 2011 |
| Language: | English |
| Physical Description: | 1 online resource (114 pages) :; illustrations some color |
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| Summary: | 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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| Bibliography: | Includes bibliographical references. |
| ISBN: | 9535155210 |
| Hierarchical level: | Monograph |
| Statement of Responsibility: | edited by Hubert Cardot and Romuald Boné. |