Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers
Michel Hell, Pyramo Costa, Fernando A. C. Gomide · IEEE Transactions on Power Delivery · 2007
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