Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)This paper introduces an evolving neural fuzzy modeling approach constructed upon the neo-fuzzy neuron and network. The approach uses an incremental learning scheme to simultaneously granulatethe input space and update the neural network weights. The neural network structure and parameters evolve simultaneously as data are input. Initially the space of each input variable is granulated using two complementary triangular membership functions. New triangular membership functions may be added, excluded and/or have their parameters adjusted depending on the input data and modeling error. The parameters of the network are updated using a gradient-based scheme with optimal learning rate. The performance of the approach is evaluated using instances of times series forecasting and nonlinear system identification problems. Computational experiments and comparisons against alternative evolving models show that the evolving neural neo-fuzzy network is accurate and fast, characteristics which are essential for adaptive systems modeling, especially in real-time, on-line environments. (C) 2013 Elsevier B. V. All rights reserved.14B194209Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Brazilian Minister of Education and InnovationConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)