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dc.contributor.editorCardot, Hubert
dc.date.accessioned2021-04-20T15:05:16Z
dc.date.available2021-04-20T15:05:16Z
dc.date.issued2011
dc.identifierONIX_20210420_9789533076850_287
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/64931
dc.description.abstractThe 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.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligenceen_US
dc.subject.otherNeural networks & fuzzy systems
dc.titleRecurrent Neural Networks for Temporal Data Processing
dc.typebook
oapen.identifier.doi10.5772/631
oapen.relation.isPublishedBy78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6
oapen.relation.isbn9789533076850
oapen.relation.isbn9789535155218
oapen.imprintIntechOpen
oapen.pages114


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