Latin American applied research
versión impresa ISSN 0327-0793
In this paper we present a nonlinear infinite impulse response (NIIR) model structure for black-box identification of nonlinear dynamic systems. The proposed model structure allows the implementation of an identification algorithm in which the degrees of freedom of the Nonlinear Output Error (NOE) model can be easily increased or decreased during the identification process. This property is very attractive to find the appropriate NIIR model, avoiding overfitting. This is done using High Level Canonical Piecewise Linear (HL CPWL) functions with an increasing (decreasing) grid division. Therefore, the algorithm may start using a linear estimation of the model. The parameters of the HL CPWL functions are updated using a simple algorithm based on a modified steepest descent method with an independently adaptive learning rate.
Palabras llave : Nonlinear Identification; NIIR Model; PWL functions.