relation: http://eprints.imtlucca.it/566/
title: Data classification and parameter estimation for the identification of piecewise affine models
creator: Bemporad, Alberto
creator: Garulli, Andrea
creator: Paoletti, Simone
creator: Vicino, Antonio
subject: QA Mathematics
subject: TA Engineering (General). Civil engineering (General)
description: This paper proposes a three-stage procedure for parametric identification of piece wise affine auto regressive exogenous (PWARX) models. The first stage simultaneously classifies the data points and estimates the number of submodels and the corresponding parameters by solving the MIN PFS problem (partition into a minimum number of feasible subsystems) for a set of linear complementary inequalities derived from input-output data. Then, a refinement procedure reduces misclassifications and improves parameter estimates. The last stage determines a polyhedral partition of the regressor set via two-class or multi-class linear separation techniques. As a main feature, the algorithm imposes that the identification error is bounded by a fixed quantity δ. Such a bound is a useful tuning parameter to trade off between quality of fit and model complexity. Ideas for efficiently addressing the MIN PFS problem, and for improving data classification are also discussed in the paper. The performance of the proposed identification procedure is demonstrated on experimental data from an electronic component placement process in a pick-and-place machine.
publisher: IEEE
date: 2004
type: Book Section
type: PeerReviewed
identifier:   Bemporad, Alberto and Garulli, Andrea and Paoletti, Simone and Vicino, Antonio  Data classification and parameter estimation for the identification of piecewise affine models.   In:  Decision and Control Conference.   IEEE, 14-17 December 2004 , pp. 20-25.  ISBN 0-7803-8682-5       (2004)  
relation: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1428600&isnumber=30836
relation: 10.1109/CDC.2004.1428600