Model structure selection is crucial for many applications that are based on identification. This paper presents a selection technique based on polygonal curve approximation to pre-process step-response data, and on a neural network classifier. Only normalized I/O data are employed, so that the network can be trained off-line with simulated data. Model-specific parameterization techniques can be envisaged so that the actual implementation of the complete identification process is not computationally intensive, and its industrial usage (e.g., for regulator autotuning) is affordable. Simulations are reported to show the effectiveness of the proposed method.
Model Structure Selection Based on Polygonal Curve Approximation Techniques
LEVA, ALBERTO;PIRODDI, LUIGI
2006-01-01
Abstract
Model structure selection is crucial for many applications that are based on identification. This paper presents a selection technique based on polygonal curve approximation to pre-process step-response data, and on a neural network classifier. Only normalized I/O data are employed, so that the network can be trained off-line with simulated data. Model-specific parameterization techniques can be envisaged so that the actual implementation of the complete identification process is not computationally intensive, and its industrial usage (e.g., for regulator autotuning) is affordable. Simulations are reported to show the effectiveness of the proposed method.File | Dimensione | Formato | |
---|---|---|---|
2006 - CDC - PiroddiLeva.pdf
Accesso riservato
Descrizione: Articolo principale
:
Publisher’s version
Dimensione
158.04 kB
Formato
Adobe PDF
|
158.04 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.