Black-box identification of a class of nonlinear systems by a recurrent neurofuzzy network.

This brief presents a structure for black-box identification based on continuous-time recurrent neurofuzzy networks for a class of dynamic nonlinear systems. The proposed network catches the dynamics of a system by generating its own states, using only input and output measurements of the system. The training algorithm is based on adaptive observer theory, the stability of the network, the convergence of the training algorithm, and the ultimate bound on the identification error as well as the parameter error are established. Experimental results are included to illustrate the effectiveness of the proposed method.