From batch to recursive outlier-robust identification of non-linear dynamic systems with neural networks

Author

Thomas, P. ; Bloch, G.

Author_Institution

Centre de Recherche en Autom., Vandoeuvre, France

Volume

1

fYear

1996

fDate

3-6 Jun 1996

Firstpage

178

Abstract

The problem of identification for nonlinear SISO systems in the presence of outliers in data is considered. Neural networks are used for their capabilities to solve nonlinear problems by learning. Three prediction error learning rules based on outlier-robust criteria are drawn up, for batch and recursive identification. The robust recursive algorithms are compared with the standard Levenberg-Marquardt update rule through a simulation example of fault detection

Keywords

learning (artificial intelligence); neural nets; nonlinear dynamical systems; prediction theory; recursive estimation; batch outlier-robust identification; fault detection; neural networks; nonlinear SISO systems; nonlinear dynamic systems; prediction error learning rules; recursive outlier-robust identification; standard Levenberg-Marquardt update rule; Artificial neural networks; Backpropagation algorithms; Fault detection; Fault diagnosis; Feedforward neural networks; Multi-layer neural network; Neural networks; Neurons; Robustness; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1996., IEEE International Conference on

Conference_Location

Washington, DC

Print_ISBN

0-7803-3210-5

Type

conf

DOI

10.1109/ICNN.1996.548887

Filename

548887