Počet záznamů: 1

Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill

  1. 1.
    0342595 - UTIA-B 2011 RIV US eng J - Článek v odborném periodiku
    Raftery, A. E. - Kárný, Miroslav - Ettler, P.
    Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill.
    Technometrics. Volume 52, Number 1 (2010), s. 52-66 ISSN 0040-1706
    Grant CEP: GA MŠk 1M0572; GA MŠk(CZ) 7D09008
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: prediction * rolling mills * Bayesian Dynamic Averaging
    Kód oboru RIV: BC - Teorie a systémy řízení
    Impakt faktor: 1.560, rok: 2010
    http://library.utia.cas.cz/separaty/2010/AS/karny-0342595.pdf http://library.utia.cas.cz/separaty/2010/AS/karny-0342595.pdf

    We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called Dynamic Model Averaging (DMA) in which a state space model for the parameters of each model is combined with a Markov chain model for the correct model. This allows the /correct" model to vary over time. The state space and Markov chain models are both specied in terms of forgetting, leading to a highly parsimonious representation. As a special case, when the model and parameters do not change, DMA is a recursive implementation of standard Bayesian model averaging, which we call recursive model averaging (RMA). The method is applied to the problem of predicting the output strip thickness for a cold rolling mill, where the output is measured with a time delay.
    Trvalý link: http://hdl.handle.net/11104/0185291