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- W1982845007 abstract "A key feature of a new recursive parameter estimator is its fast convergence. Here averaging theory supports this claim of fast convergence for system parameters, which heretofore has been supported only by example and by analogy with optimal nonlinear filtering. Averaging theory is also used to investigate possible computational shortcuts. Finally, it is proven that the likelihood function has a unique maximum for stable, observable, single output systems in state space form when the parameters are asymptotically identifiable and true values are in the model set. Then the new parameter estimator must converge globally with probabiltity one to true parameter values when the trajectories of the parameter estimates remain in a certain bounded set." @default.
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- W1982845007 date "2011-01-01" @default.
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- W1982845007 title "A New Convergent Approximation of the Optimal Recursive Parameter Estimator: Averaged Asymptotic Dynamic Analysis" @default.
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- W1982845007 doi "https://doi.org/10.3182/20110828-6-it-1002.00362" @default.
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