Modelisation of Linear and Nonlinear Systems Using Learning Methods

Abstract This paper presents an identification methodology based on a single model deduced from Neural network theory. It measures and analyses the degree of precision obtained in feedforward propagation and back-propagation, and defines the influence parameters for convergence of network errors: the scale factor influence and the polynomial degree influence.