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Type: Artigo de periódico
Title: Recurrent neurofuzzy network in thermal modeling of power transformers
Author: Hell, M
Costa, P
Gomide, F
Abstract: This work suggests recurrent neurofuzzy networks as a means to model the thermal condition of power transformers. Experimental results with actual data reported in the literature show that neurofuzzy modeling requires less computational effort, and is more robust and efficient than multilayer feedforward networks, a radial basis function network, and classic deterministic modeling approaches.
Subject: power transformers
recurrent neurofuzzy networks (RNFNs)
thermal modeling
Country: EUA
Editor: Ieee-inst Electrical Electronics Engineers Inc
Citation: Ieee Transactions On Power Delivery. Ieee-inst Electrical Electronics Engineers Inc, v. 22, n. 2, n. 904, n. 910, 2007.
Rights: fechado
Identifier DOI: 10.1109/TPWRD.2006.874613
Date Issue: 2007
Appears in Collections:Unicamp - Artigos e Outros Documentos

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