A Bayesian approach to identification of hybrid systems

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35 Citations (Scopus)


In this paper we present a novel procedure for the identification of hybrid systems in the piece-wise ARX form. The procedure consists of three steps: 1) parameter estimation, 2) classification of data points and 3) partition estimation. Our approach to parameter estimation is based on the gradual refinement of the a-priori information about the parameter values, using the Bayesian inference rule. Particle filters are used for a numerical implementation of the proposed parameter estimation procedure. Data points are subsequently classified to the mode which is most likely to have generated them. A modified version of the multi-category robust linear programming (MRLP) classification procedure, adjusted to use the information derived in the previous steps of the identification algorithm, is used for estimating the partition of the PWARX map. The proposed procedure is applied for the identification of the component placement process in pick-and-place machines.

Original languageEnglish
Title of host publication43rd IEEE Conference on Decision and Control
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages7
Publication statusPublished - 1 Dec 2004
Event43rd IEEE Conference on Decision and Control (CDC 2004) - "Atlantis", Nassau, Bahamas
Duration: 14 Dec 200417 Dec 2004
Conference number: 43


Conference43rd IEEE Conference on Decision and Control (CDC 2004)
Abbreviated titleCDC 2004
Internet address


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