Black-box input-output identification of a class of nonlinear systems using a discrete-time recurrent neurofuzzy network

Abstract

From theory based on adaptive observers, this paper presents a structure for black-box identification based on state-space recurrent neural networks for a class of dynamic nonlinear systems in discrete-time. The network catches the dynamics of the unknown plant and jointly identifies its parameters using only output measurements. The stability and the convergence of the training algorithm and the ultimate bound on the identification error as well as the parameter error are established in the Lyapunov sense. Numerical examples using simulated and experimental systems are included to demonstrate the effectiveness of the proposed method. © 2010 Springer-Verlag Berlin Heidelberg.