On Identifying Global Nonlinear Discrete Models from Chaotic Data

Abstract

This paper investigates the identification of global discrete nonlinear models from chaotic data. It is shown that when chaotic data from a nonlinear system do not contain enough information about all the fixed points, the usual model selection procedures can select different local nonlinear models.