Improved learning algorithm for two-layer neural networks for identification of nonlinear systems

This study is concerned with the asymptotic identification of nonlinear systems based on Lyapunov theory and two-layer neural networks. An improved identification model enhanced with a feedback term and a novel adaptation law for the threshold offset, associated with the output weight matrix, is introduced to assure the convergence of the online prediction error, even in the presence of approximation error and bounded disturbances and when upper bounds for these perturbations are not known in advance. The effectiveness of the proposed method and its application to the identification of a hyperchaotic system and control of a welding system is investigated. (C) 2018 Elsevier B.V. All rights reserved.