Comparison of four procedures for the identification of hybrid systems

Authors:
Aleksandar Lj. Juloski;W. P. M. H. Heemels;Giancarlo Ferrari-Trecate;René Vidal;Simone Paoletti;J. H. G. Niessen
Affiliations:
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands;Embedded Systems Institute, Eindhoven, The Netherlands;INRIA, Le Chesnay Cedex, France;Center for Imaging Science, Johns Hopkins University, Baltimore, MD;Dipartimento di Ingegneria dell'Informazione, Universita' di Siena, Siena, Italy;Nyquist, Industrial Control, Eindhoven, The Netherlands
Venue:
HSCC'05 Proceedings of the 8th international conference on Hybrid Systems: computation and control
Year:
2005

Citing 7
Cited 13

Optimal estimation theory for dynamic systems with set membership uncertainty: an overview

Automatica (Journal of IFAC)
The MIN PFS problem and piecewise linear model estimation

Discrete Applied Mathematics - Special issue: Third ALIO-EURO meeting on applied combinatorial optimization
A greedy approach to identification of piecewise affine models

HSCC'03 Proceedings of the 6th international conference on Hybrid systems: computation and control
Conditions of optimal classification for piecewise affine regression

HSCC'03 Proceedings of the 6th international conference on Hybrid systems: computation and control
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
A clustering technique for the identification of piecewise affine systems

Automatica (Journal of IFAC)
Identification of piecewise affine systems via mixed-integer programming

Automatica (Journal of IFAC)

Switching systems: active mode recognition, identification of the switching law

Journal of Control Science and Engineering
Switched and PieceWise Nonlinear Hybrid System Identification

HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Parameter Synthesis for Piecewise Affine Systems from Temporal Logic Specifications

HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Information Theoretical Approach to Identification of Hybrid Systems

HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Identifiability of discrete-time linear switched systems

Proceedings of the 13th ACM international conference on Hybrid systems: computation and control
A stochastic framework for hybrid system identification with application to neurophysiological systems

HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Switch detection in genetic regulatory networks

HSCC'07 Proceedings of the 10th international conference on Hybrid systems: computation and control
Review:

The Knowledge Engineering Review
A k-plane clustering algorithm for identification of hybrid systems

ACMOS'06 Proceedings of the 8th WSEAS international conference on Automatic control, modeling & simulation
Reconstruction of switching thresholds in piecewise-affine models of genetic regulatory networks

HSCC'06 Proceedings of the 9th international conference on Hybrid Systems: computation and control
Formal analysis of piecewise affine systems through formula-guided refinement

Automatica (Journal of IFAC)
Identification of switched linear regression models using sum-of-norms regularization

Automatica (Journal of IFAC)
Hybrid-fuzzy modeling and identification

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we compare four recently proposed procedures for the identification of PieceWise AutoRegressive eXogenous (PWARX) and switched ARX models. We consider the clustering-based procedure, the bounded-error procedure, and the Bayesian procedure which all identify PWARX models. We also study the algebraic procedure, which identifies switched linear models. We introduce quantitative measures for assessing the quality of the obtained models. Specific behaviors of the procedures are pointed out, using suitably constructed one dimensional examples. The methods are also applied to the experimental identification of the electronic component placement process in pick-and-place machines.