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Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers

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3 Author(s)
Hell, M. ; Dept. ofComputer Eng. & Autom., State Univ. of Campinas ; Costa, P. ; Gomide, F.

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

Published in:

Power Delivery, IEEE Transactions on  (Volume:22 ,  Issue: 2 )

Date of Publication:

April 2007

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