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dc.contributor.authorCaruso, M.
dc.contributor.authorJarne, Cecilia
dc.date2023
dc.date.accessioned2023-10-16T13:03:03Z
dc.date.available2023-10-16T13:03:03Z
dc.identifier.citationCaruso, M., Jarne, C. (2023). Recurrent Neural Networks as Electrical Networks, a Formalization. In: Machado, J.M., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-031-23210-7_10es_ES
dc.identifier.isbn9783031232091
dc.identifier.issn2367-3370
dc.identifier.urihttps://reunir.unir.net/handle/123456789/15401
dc.description.abstractSince the 1980s, and particularly with the Hopfield model, recurrent neural networks or RNN became a topic of great interest. The first works of neural networks consisted of simple systems of a few neurons that were commonly simulated through analogue electronic circuits. The passage from the equations to the circuits was carried out directly without justification and subsequent formalisation. The present work shows a way to formally obtain the equivalence between an analogue circuit and a neural network and formalizes the connection between both systems. We also show which are the properties that these electrical networks must satisfy. We can have confidence that the representation in terms of circuits is mathematically equivalent to the equations that represent the network.es_ES
dc.language.isoenges_ES
dc.publisherLecture Notes in Networks and Systemses_ES
dc.relation.urihttps://link.springer.com/chapter/10.1007/978-3-031-23210-7_10es_ES
dc.rightsrestrictedAccesses_ES
dc.subjectelectrical networkses_ES
dc.subjectformalizationes_ES
dc.subjectRNNes_ES
dc.subjectScopus(2)es_ES
dc.titleRecurrent Neural Networks as Electrical Networks, a Formalizationes_ES
dc.typeconferenceObjectes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.1007/978-3-031-23210-7_10


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