Recursive learning algorithms for training fuzzy recurrent models

A recurrent fuzzy neural network with external feedback, called the Dynamical-Adaptive Fuzzy Neural Network (D-AFNN), is proposed in this article for adapting discrete-time dynamical systems. The fuzzy model is based on the Takagi-Sugeno inference method with polynomial consequent functions. The D-AFNN model is trained to learn the system dynamics using a recursive training algorithm. Both the epochwise and the on-line version of the recursive algorithm are considered. The standard representation of a dynamical system, using the fuzzy recurrent model, is discussed and the computational procedure for the derivation of the output gradients is analytically described. The performance qualities of the D-AFNN model are illustrated with two examples. In the first example, the model is trained to identify a nonlinear plant, while in the second the fuzzy recurrent model implements an IIR adaptive filter for noise attenuation. © 1996 John Wiley & Sons, Inc.