A novel recurrent, hybrid neurofuzzy network is proposed in this paper. This model is composed by two distinct parts: a fuzzy inference system and a neural network. The fuzzy system is constructed from fuzzy set models whose units of the fuzzy system are modeled through triangular norms and co-norms, and weights defined within the unit interval. The neural network contain classical nonlinear neurons. The hybrid system has a multilayer, recurrent structure. The learning procedure developed is based on two main paradigms; associative reinforcement learning and gradient search. These learning algorithms are associated to the fuzzy system and neural network, respectively. That is, output layer weights are adjusted via an error gradient method whereas a reward and punishment scheme updates the hidden layer weights. The recurrent neurofuzzy network is used to develop models of a nonlinear processes. Numerical results show that the neurofuzzy network proposed here provides accurate models after short period of learning time.1 785790Santos, E.P., Von Zuben, F.J., Efficient second-order learning algorithms for discrete-time recurrent neural networks (1999) Recurrent Neural Networks: Design and Applications, pp. 47-75. , CRC PressL. R. Medsker and L. C. Jain, EdsKu, C.C., Lee, K.L., Diagonal recurrent neural networks for dynamic systems control (1995) IEEE Trans. on Neural Networks, 6, pp. 144-156. , JanuaryWilliams, R.J., Zipser, D., A learning algorithm for continually running fully recurrent neural networks (1989) Neural Computation, 1, pp. 270-280Lee, C.H., Teng, C.C., Identification and control of dynamic systems using recurrent fuzzy neural networks (2000) IEEE Trans. on Fuzzy Systems, 8 (4), pp. 3493-3666. , AugustBuckley, A., Hayashi, Y., Fuzzy neural networks: A survey (1994) Fuzzy Sets and Systems, 66, pp. 41-49Nürnberger, A., Radetzky, A., Kruse, R., Using recurrent neuro-fuzzy techniques for the identification and simulation of dynamic systems (2001) Neurocomputing, 36, pp. 123-147Nürnberger, A., A hierarchical recurrent neuro-fuzzy system Proc. of Joint 9th IFSA World Congress and 20th NAFIPS International Conference, IEEE, 2001, pp. 1407-1412Ballini, R., Soares, S., Gomide, F., A recorrent neurofuzzy network structure and learning procedure (2001) 10th IEEE International Conference on Fuzzy Systems - FUZZ-IEEE'2001, 3Caminhas, W., Tavares, H., Gomide, F., Pedrycz, W., Fuzzy set based neural networks: Structure, learning and application (1999) Journal of Advanced Computational Intelligence, 3 (3), pp. 151-157Pedrycz, W., Rocha, A., Fuzzy-set based models of neuron and knowledge-based networks (1998) IEEE Transactions on Fuzzy Systems, 4 (1), pp. 254-266Pedrycz, W., Gomide, F., (1998) An Introduction to Fuzzy Sets: Analysis and Design, , MIT Press, Cambridge, MABarto, A.G., Jordan, M.I., Gradient following without back-propagation in layered networks Proceedings of the IEEE First International Conference on Neural Networks, San Diego, 1987, 2, pp. 629-636Nozaki, K., Ishibuchi, H., Tanaka, H., Trainable fuzzy classification systems based on fuzzy if-then rules Proc. of the Third IEEE International Conference on Fuzzy Systems, 1994, pp. 408-502Oliveira, M., Figueiredo, M., Gomide, F., A neurofuzzy approach for autonomous control Proc. of the Third IEEE International Conference on Fuzzy Systems, Neural Nets and Soft Computing, 1994, pp. 597-598Rumelhart, D., Hinton, G.E., Williams, R.J., Learning representations by back-propagating errors (1986) Nature (London), 323, pp. 533-536Jacobs, R.A., Increased rates of convergence through learning rate adaptation (1988) Neural Networks, 1, pp. 295-307Narendra, K.S., Parthasarathy, K., Identification and control of dynamical systems using neural networks (1990) IEEE Transactions on Neural Networks, 1 (1-4)Wang, L.X., (1994) Adaptive Fuzzy Systems and Control, , Prentice-Hall