Parameter tracking with partial forgetting method

0370448 - ÚTIA 2012 RIV GB eng J - Journal Article
Dedecius, Kamil - Nagy, Ivan - Kárný, Miroslav
Parameter tracking with partial forgetting method.
International Journal of Adaptive Control and Signal Processing. Roč. 26, č. 1 (2012), s. 1-12. ISSN 0890-6327. E-ISSN 1099-1115
R&D Projects: GA ČR GA102/08/0567
Institutional research plan: CEZ:AV0Z10750506
Keywords : regression models * model * parameter estimation * parameter tracking
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 1.219, year: 2012
http://library.utia.cas.cz/separaty/2012/AS/dedecius-0370448.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0204249