@incollection{eprints554,
       publisher = {Elsevier},
           pages = {1789--1794},
         journal = {13th IFAC Symposium on System Identification},
          author = {Alberto Bemporad and Andrea Garulli and Simone Paoletti and Antonio Vicino},
            year = {2003},
          editor = {Paul Van Den Hof and Bo Wahlberg and Siep Weiland},
           title = {Set membership identification of piecewise affine models},
       booktitle = {System Identification 2003  : a proceeding volume from the 13th IFAC Symposium on System Identification},
             url = {http://eprints.imtlucca.it/554/},
        abstract = {This paper addresses the problem of identification of piecewise affine (PWA)models, which involves the joint estimations of both the parameters of the affine submodels and the partition of the PWA map from data. According to ideas from set-membership identification, the key approach is to characterize the model by its maximum allowed prediction error, which is used as a tuning knob for traning off between prediction accuracy and model complexity. At initialization, the proposed procedure for PWA identification exploits a technique per partitioning an infeasible system of linear inequalities into a (possibly minimum) number of feasible subsystems. This provides both an initial clustering of the datapoints and a guess of the number of required submodels, which therefore is not fixed a priori. A refinement procedure is then applied in order to improve both data classification and parameter estimation. The partition of the PWA maps is finally estimated by considering multicategory classification techniques.},
        keywords = {Nonlinear identification; hybrid system; bounded error; data classification; parameter estimation}
}