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Bibliography

Journal Article

Parameter tracking with partial forgetting method

Dedecius Kamil, Nagy Ivan, Kárný Miroslav

: International Journal of Adaptive Control and Signal Processing vol.26, 1 (2012), p. 1-12

: CEZ:AV0Z10750506

: GA102/08/0567, GA ČR

: regression models, model, parameter estimation, parameter tracking

: 10.1002/acs.1270

: http://library.utia.cas.cz/separaty/2012/AS/dedecius-0370448.pdf

(eng): 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.

: BB

2019-01-07 08:39