Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/93354
Type: Artigo de evento
Title: Recurrent Neural Approaches For Power Transformers Thermal Modeling
Author: Hell M.
Secco L.
Costa Jr. P.
Gomide F.
Abstract: This paper introduces approaches for power transformer thermal modeling based on two conceptually different recurrent neural networks. The first is the Elman recurrent neural network model whereas the second is a recurrent neural fuzzy network constructed with fuzzy neurons based on triangular norms. These two models are used to model the thermal behavior of power transformers using data reported in literature. The paper details the neural modeling approaches and discusses their main capabilities and properties. Comparisons with the classic deterministic model and static neural modeling approaches are also reported. Computational experiments suggest that the recurrent neural fuzzy-based modeling approach outperforms the remaining models from both, computational processing speed and robustness point of view. © Springer-Verlag Berlin Heidelberg 2005.
Editor: 
Rights: fechado
Identifier DOI: 10.1007/11590316_41
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-33646720117&partnerID=40&md5=683cc0457a7bb34603f1f469d25191c7
Date Issue: 2005
Appears in Collections:Unicamp - Artigos e Outros Documentos

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