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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.

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This paper presents an approach for time series forecasting using a new class of fuzzy neural networks called uninetworks. Uninetworks are constructed using a recent generalization of the classic and and or logic neurons. These generalized logic neurons, called unineurons, provide a mechanism to implement general nonlinear processing and introduce important characteristics of biological neurons such as neuronal AND synaptic plasticity. Unineurons achieve synaptic and neuronal plasticity modifying their internal parameters in response to external changes. Thus, unineurons may individually vary from an and neuron to an or neuron (and vice-versa), depending upon the necessity of the modeling task. Besides, the proposed neural fuzzy networks are able to extract knowledge from input/output data and to encode it explicitly in the form of if-then rules. Therefore, linguistic models are obtained in a form suitable for human understanding. Experimental results show that the models proposed here are more general and perform best in terms of accuracy and computational costs when compared against alternative approaches suggested in the literature.