Neurofuzzy networks come to be a powerful alternative strategy to develop fuzzy systems, since they are capable of learning and providing IF-THEN fuzzy rules in linguistic or explicit form. Amongst such models, ANFIS has been recognized as a reference framework, mainly for its flexible and adaptive character. In this paper, we extend ANFIS theory by experimenting with a multi-net approach wherein two or more differently structured ANFIS instances are coupled to play together. Ensembles of ANFIS (E-ANFIS) enhance ANFIS performance skills as well as alleviate some of its computational bottlenecks. Moreover, it promotes the automatic configuration of different ANFIS units and the a posteriori selective combination of their outputs. Experiments conducted to assess E-ANFIS generalization capability are also presented.1 506511Lin, C.-T., George Lee, C.S., (1996) Neural Fuzzy Systems: Neuro-Fuzzy Synergism to Intelligent Systems, , Prentice-Hall PTRFigueiredo, M., Gomide, F., Design of fuzzy systems using neurofuzzy networks (1999) IEEE Trans. on Neural Networks, 10 (4), pp. 815-827. , JulyKasabov, N., Kim, J., Watts, M., Gray, A., A fuzzy neural network architecture for adaptive learning and knowledge acquisition (1996) Information Sciences, 101 (3-4), pp. 155-175Nauck, D., Kruse, R., Neuro-fuzzy systems for function approximation (1999) Fuzzy Sets and Systems, 101, pp. 261-277Jang, J.-S., ANFIS: Adaptive-network-based fuzzy inference system (1993) IEEE Trans. on System, Man, and Cybernetics, 23 (3), pp. 665-685. , May/JuneJang, J.-S., Sun, C., Neuro-fuzzy modeling and control (1995) Proceedings of the IEEE, 83 (3), pp. 378-406Sharkey, A., Combining artificial neural nets: Ensemble and modular multi-net systems (1999), Springer-Verlag, LondonOpitz, D., Maclin, R., Popular ensemble methods: An empirical study (1999) Journal of Artificial Intelligence Research, 11, pp. 169-198Coelho, A.L.V., On the cooperation of fuzzy neural networks via a coevolutionary approach (2001) Procs. of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, pp. 785-790. , Vancouver, Canada, JulyBaxt, W.G., Improving the accuracy of an artificial neural network using multiple differently trained networks (1992) Neural Computation, 4 (5), pp. 135-144Hashem, S., Schmeiser, B., Improving model accuracy using optimal linear combinations of trained neural networks (1995) IEEE Transactions on Neural Networks, 6 (3), pp. 792-794Hashem, S., Optimal linear combinations of neural networks (1997) Neural Network, 10 (4), pp. 599-614Takagi, T., Sugeno, M., Fuzzy identification of systems and its applications to modeling and control (1985) IEEE Transactions on Systems, Man, and Cybernetics, 15 (1), pp. 116-132. , Jan/FebJordan, M., Jacobs, R., Hierarchical mixtures of experts and the EM algorithm (1994) Neural Computation, 6 (2), pp. 181-214. , MarchPerrone, M.P., Cooper, L.N., When networks disagree: Ensemble method for neural networks (1993) Neural Networks for Speech and Image Processing, pp. 126-142. , In Mammone, R. J., editorChapman-Hall