%0 Journal Article
%T Stability Analysis of A Neuro-Identification Scheme with Asymptotic Convergence
%A Josek A. R. Vargas
%A Elder M. Hemerly
%A Elmer R. L. Villarreal
%J International Journal of Artificial Intelligence & Applications
%D 2012
%I Academy & Industry Research Collaboration Center (AIRCC)
%X This paper focuses on the stability and convergence analysis of a neuro-identification scheme for uncertainnonlinear systems. Based on linearly parameterized neural networks and the previous knowledge of upperbounds for the approximation error and disturbances, a robust modification of the descent gradientalgorithm is proposed to make the overall identification process stable, and, in addition, the on-lineresidual prediction error asymptotically null, despite the presence of approximation error anddisturbances. A simulation study to show the application and comparative performance of the proposedalgorithm is presented
%K On-line identification
%K nonlinear estimators
%K uncertain systems
%K neural networks.
%U http://airccse.org/journal/ijaia/papers/3412ijaia03.pdf