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Bayesian M-T clustering for reduced parameterisation of Markov chains used for non-linear adaptive elements

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    0410587 - UTIA-B 20010056 RIV GB eng J - Journal Article
    Valečková, Markéta - Kárný, Miroslav - Sutanto, E. L.
    Bayesian M-T clustering for reduced parameterisation of Markov chains used for non-linear adaptive elements.
    Automatica. Roč. 37, č. 6 (2001), s. 1071-1078. ISSN 0005-1098. E-ISSN 1873-2836
    R&D Projects: GA ČR GA102/99/1564
    Grant - others:IST(XE) 1999/12058
    Institutional research plan: AV0Z1075907
    Keywords : Markov chain * clustering * Bayesian mixture estimation
    Subject RIV: BC - Control Systems Theory
    Impact factor: 1.449, year: 2001

    Markov chains are black box models ideal for describing stochastic digitised systems. Although the identification of their parameters can be a relatively easy task to perform, the dimensionality involved become undesirable large. This significant drawback can be overcome by exploiting smoothness of the underlying system. The paper present a novel hybrid off-line algorithm to locate areas which merit detailed model description. It comprises Bayesian parameter estimation and Mean tracking algorithm.
    Permanent Link: http://hdl.handle.net/11104/0130676

     
     

Number of the records: 1  

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