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dc.contributor.CRUESPUNIVERSIDADE DE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbn3540305068; 9783540305064pt
dc.typeArtigo de eventopt_BR
dc.titleRecurrent Neural Approaches For Power Transformers Thermal Modelingpt_BR
dc.contributor.authorHell M.pt_BR
dc.contributor.authorSecco L.pt_BR
dc.contributor.authorCosta Jr. P.pt_BR
dc.contributor.authorGomide F.pt_BR
unicamp.authorHell, M., State University of Campinas, UNICAMP, Av. Albert Einstein, 400, 13083-852, Campinas, SP, Brazilpt_BR
unicamp.authorGomide, F., State University of Campinas, UNICAMP, Av. Albert Einstein, 400, 13083-852, Campinas, SP, Brazilpt_BR
unicamp.author.externalSecco, L., Pontifical Catholic University of Minas Gerais, PUC-MG, Av. Dom Jose Caspar, 500, Predio 3, 30535-610, Belo Horizonte, MG, Brazilpt
unicamp.author.externalCosta Jr., P., Pontifical Catholic University of Minas Gerais, PUC-MG, Av. Dom Jose Caspar, 500, Predio 3, 30535-610, Belo Horizonte, MG, Brazilpt
dc.description.abstractThis 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.en
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)pt_BR
dc.publisherpt_BR
dc.date.issued2005pt_BR
dc.identifier.citationLecture Notes In Computer Science (including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics). , v. 3776 LNCS, n. , p. 287 - 293, 2005.pt_BR
dc.language.isoenpt_BR
dc.description.volume3776 LNCSpt_BR
dc.description.issuenumberpt_BR
dc.description.initialpage287pt_BR
dc.description.lastpage293pt_BR
dc.rightsfechadopt_BR
dc.sourceScopuspt_BR
dc.identifier.issn3029743pt_BR
dc.identifier.doi10.1007/11590316_41pt_BR
dc.identifier.urlhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33646720117&partnerID=40&md5=683cc0457a7bb34603f1f469d25191c7pt_BR
dc.date.available2015-06-26T14:07:27Z
dc.date.available2015-11-26T15:41:45Z-
dc.date.accessioned2015-06-26T14:07:27Z
dc.date.accessioned2015-11-26T15:41:45Z-
dc.description.provenanceMade available in DSpace on 2015-06-26T14:07:27Z (GMT). No. of bitstreams: 1 2-s2.0-33646720117.pdf: 360894 bytes, checksum: 432f6a493cc1e3c064ddff12f3eea188 (MD5) Previous issue date: 2005en
dc.description.provenanceMade available in DSpace on 2015-11-26T15:41:45Z (GMT). No. of bitstreams: 2 2-s2.0-33646720117.pdf: 360894 bytes, checksum: 432f6a493cc1e3c064ddff12f3eea188 (MD5) 2-s2.0-33646720117.pdf.txt: 15639 bytes, checksum: 422c3bb8cea48efdb2391193396da6a6 (MD5) Previous issue date: 2005en
dc.identifier.urihttp://www.repositorio.unicamp.br/handle/REPOSIP/93354
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/93354-
dc.identifier.idScopus2-s2.0-33646720117pt_BR
dc.description.referenceSwift, G.W., Adaptative transformer thermal overload protection (2001) IEEE Transactions on Power Delivery, 16 (4), pp. 516-521pt_BR
dc.description.referenceGaldi, V., Ippolito, L., Piccolo, A., Vaccaro, A., Neural diagnostic system for transformer thermal overload protection (2000) IEE Proceedings of Electric Power Applications, 147 (5), pp. 415-421pt_BR
dc.description.referenceNarendra, K.S., Parthasarathy, K., Identification and control of dynamic systems using neural networks (1990) IEEE Transactions on Neural Networks, 1, pp. 4-27pt_BR
dc.description.referenceHaykin, S., (1998) Neural Networks: A Comprehensive Foundation, , Prentice Hall, NJ-USA, ed. 2pt_BR
dc.description.referenceJang, J.-S.R., ANFIS: Adaptative-network-based fuzzy inference system (1993) IEEE Transactions on System, Man, and Cybernetics, 23 (3), pp. 665-685pt_BR
dc.description.referenceElman, J., Finding structure in time (1990) Cognitive Science, 14, pp. 179-211pt_BR
dc.description.referenceBallini, R., Gomide, F., Learning in recurrent, hybrid neurofuzzy networks (2002) IEEE International Conference on Fuzzy Systems, pp. 785-791pt_BR
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