Robust adaptive identification of nonlinear system using neural network

Abstract

It is well-known that disturbances can cause the divergence of neural networks in the identification of nonlinear systems. Sufficient conditions using so-called modified algorithms are available to provide guaranteed convergence for adaptive systems. They are: the dead-zone scheme, adaptive law modification and /spl sigma/-modification. These schemes normally require knowledge of the upper bound of the disturbance. In this paper, a robust weight-tuning algorithm is used to train a multi-layered neural network with an adaptive dead-zone scheme. The proposed robust adaptive algorithm does not require knowledge of either the upper bound of the disturbance or the bound on the norm of the estimate parameter. A complete convergence is provided based on the Lyapunov theorem to deal with the nonlinear system. Simulation results are presented to show the good performance of the algorithm.