0370448 20240103200153.920120109235959.9 000298786900001 84855462282 10.1002/acs.1270 12 s. cav_un_epca*02567720890-6327261 (2012)1-12Wiley regression models model parameter estimation parameter tracking cav_un_auth*0242543 Dedecius Kamil Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101167 Nagy Ivan Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Signal Processing Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101124 Kárný Miroslav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2012/AS/dedecius-0370448.pdf GA102/08/0567 GA ČR cav_un_auth*0239566 CEZ:AV0Z10750506 This paper concerns the Bayesian tracking of slowly varying parameters of a linear stochastic regression model. The modelled and predicted system output is assumed to possess time-varying mean value, whereas its dynamics are relatively stable. The proposed estimation method models the system output mean value by time-varying offset. It formulates three extreme hypotheses on model parameters’ variability: (i) no parameter varies; (ii) all parameters vary; and (iii) the offset varies. The Bayesian paradigm then provides a mixture as posterior distribution, which is appropriately projected to a feasible class. Exponential forgetting at ‘second’ hypotheses level allows tracking of slow variations of respective hypotheses. 2012 BB 3 4 A 4a 20231122134847.8 http://hdl.handle.net/11104/0204249 AUTOMATIONCONTROLSYSTEMS|ENGINEERINGELECTRICALELECTRONIC 1.334 0.323 6 0.00262 0.522 809 65 0.779 Q1 48.308 52.469 Q3 Q2 2012 Soubory v repozitáři: dedecius-0370448.pdf 84855462282 SCOPUS 000298786900001 WOS cav_un_epca*0256772 International Journal of Adaptive Control and Signal Processing 0890-6327 1099-1115 Roč. 26 č. 1 2012 1 12 Wiley