Identification of piecewise affine systems based on Dempster-Shafer Theory

Abstract In this paper, we present a novel approach for the identification of Piecewise ARX systems from input-output data. The proposed method is able to solve simultaneously the problem of the data assignment, the parameter estimation and the submodels’ number thanks to an evidential procedure. Our approach exploits the fact that, in PWARX systems, the data are locally linear. That's why our strategy aims at simultaneously minimizing the error between the measured output and each submodel's output and the distance between data belonging to the same submodel. A soft multi-category Support Vector Classifier is used to find a complete polyhedral partition of the regressor set by discriminating all the classes simultaneously. Finally, the performance and the feasibility of the proposed approach are proved via a numerical example and an experimental data collected from an electronic component placement process in a pick-and-place machine.