On-line estimation of dynamic shock-error models based on the Kullback Leibler information measure

TitleOn-line estimation of dynamic shock-error models based on the Kullback Leibler information measure
Publication TypeJournal Article
Year of Publication1994
AuthorsKrishnamurthy, V.
JournalAutomatic Control, IEEE Transactions on
Pagination1129 -1135
Date Publishedmay.
Keywordscomputational costs, dynamic shock-error models, errors-in-variables models, expectation-maximization algorithm, gradient-based scheme, information theory, jump changes, Kalman filtering techniques, Kalman filters, Kullback Leibler information measure, log likelihood, maximum likelihood estimation, negligible memory requirements, numerical analysis, off-line maximum likelihood estimation, online estimation, recursive Gauss-Newton scheme, state-space methods, state-space representation, stochastic approximations, time domain

Develops two sequential or ldquo;on-line rdquo; estimation schemes in the time domain for dynamic shock-error models which are special cases of errors-in-variables models. The author's approach utilizes a state-space representation of the model, Kalman filtering techniques, and on-line algorithms. The first on-line algorithm is based on the expectation-maximization algorithm and uses a recursive Gauss-Newton scheme to maximize the Kullback Leibler information measure. The second on-line algorithm the author proposes is a gradient-based scheme and uses stochastic approximations to maximize the log likelihood. In comparison to the off-line maximum likelihood estimation scheme used in Ghosh (1989), the author's on-line algorithms have significantly reduced computational costs and negligible memory requirements. Simulations illustrate the satisfactory performance of the algorithms in estimating errors-in-variables systems with parameters that vary slowly with time or undergo infrequent jump changes


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