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Log-Normal Merging for Distributed System Identification

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    SYSNO ASEP0328917
    Document TypeC - Proceedings Paper (int. conf.)
    R&D Document TypeConference Paper
    TitleLog-Normal Merging for Distributed System Identification
    TitleLognormální skládání pravděpodobností pro distribuovanou identifikaci systémů
    Author(s) Šmídl, Václav (UTIA-B) RID, ORCID
    Kárný, Miroslav (UTIA-B) RID, ORCID
    Source TitleProceedings of the 15th IFAC Symposium on System Identification. - Saint Malo : IFAC, 2009
    Pagess. 1-6
    Number of pages6 s.
    Publication formwww - www
    Action15th IFAC Symposium on System Identification
    Event date06.07.2009-08.07.2009
    VEvent locationSaint Malo
    CountryFR - France
    Event typeWRD
    Languageeng - English
    CountryFR - France
    KeywordsBayesian Methods ; Hybrid and Distributed System Identification ; Particle Filtering/Monte Carlo Methods
    Subject RIVBC - Control Systems Theory
    R&D Projects1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
    GP102/08/P250 GA ČR - Czech Science Foundation (CSF)
    GA102/08/0567 GA ČR - Czech Science Foundation (CSF)
    CEZAV0Z10750506 - UTIA-B (2005-2011)
    AnnotationGrowing interest in applications of distributed systems, such as multi-agent systems, increases demands on identification of distributed systems from partial information sources collected by local agents. We are concerned with fully distributed scenario where system is identified by multiple agents, which do not estimate state of the whole system but only its local `state'. The resulting estimate is obtained by merging of marginal and conditional posterior probability density functions (pdf) on such local states. We investigate the use of recently proposed non-parametric log-normal merging of such `fragmental' pdfs for this task. We derive a projection of the optimal merger to the class of weighted empirical pdfs and mixtures of Gaussian pdfs. We illustrate the use of this technique on distributed identification of a controlled autoregressive model.
    WorkplaceInstitute of Information Theory and Automation
    ContactMarkéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
    Year of Publishing2010
Number of the records: 1  

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