A recurrent neuro-fuzzy network structure and learning procedure

A novel recurrent neurofuzzy network is proposed in this paper. This model is constructed from fuzzy set models of neurons. The network has a multilayer, recurrent structure whose units are modeled through triangular norms and conorms, and weights are defined within the unit interval. The learning procedure developed is based on two main paradigms, the gradient search and associative reinforcement learning, that is, the output layer weights are adjusted via an error gradient method whereas a reward and punishment scheme updates the hidden layer weights. The recurrent neuro-fuzzy network is used to develop a model of a nonlinear process. Numerical results show that the neuro-fuzzy network proposed provides an accurate process model after a short period of learning time