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On Duality of Regularized Exponential and Linear Forgetting

Author(s):

R. Kulhavy, F. Kraus
Conference/Journal:

vol. AUT95-14
Abstract:

Regularized (stabilized) versions of exponential and linear forgetting in parameter tracking are shown to be dual to each other. Both are derived by solving basically the same Bayesian decision-making problem where Kullback-Leibler divergence is used to measure (quasi)distance between probability distributions of estimated parameters. The type of forgetting depends solely on the order of arguments in Kullback-Leibler divergence. This general view indicates under which conditions one technique is superior to the other. Applied to the case of ARX models, the approach results in a class of regularized or stabilized forgetting strategies that are naturally robust with respect to poor system excitation.

Year:

1995
Type of Publication:

(04)Technical Report
Supervisor:



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% Autogenerated BibTeX entry
@TechReport { KulKra:1995:IFA_1486,
    author={R. Kulhavy and F. Kraus},
    title={{On Duality of Regularized Exponential and Linear Forgetting}},
    institution={},
    year={1995},
    number={},
    address={},
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=1486}
}
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