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...
Tallennettuna:
| Muut tekijät: | |
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| Aineistotyyppi: | Elektroninen Kirjan osa |
| Kieli: | englanti |
| Julkaistu: |
IntechOpen
2011
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| Aiheet: | |
| Linkit: | DOAB: download the publication DOAB: description of the publication |
| Tagit: |
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| Yhteenveto: | 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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| Ulkoasu: | 1 electronic resource (114 p.) |
| ISBN: | 631 9789533076850 9789535155218 |
| Pääsy: | Open Access |