Nonlinear system identification using embedded dynamic neural networks

Abstract

Identification of a class of nonlinear systems by using two neuro-dynamic structures is addressed. The capabilities of the proposed structures for representing the class of system considered are shown analytically. Selection criteria for specifying the fixed structural parameters and adaptation laws for updating the adjustable parameters are provided. Numerical simulation results are also provided to illustrate the performance of the proposed structures.