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- W2734030405 abstract "In this paper we investigate the fundamental limits of the identification of linear time-varying systems in the presence of noise. We consider this problem for the case where the uncertain system is known a priori to belong to a set of linear time-varying systems that have a uniform bound on their length of memory, and a uniform bound on their rate-of-variation. The identification criterion we treat is worst-case with respect to both noise and system modelling uncertainty. We determine bounds on the optimal (minimum) worst-case identification error that can be achieved asymptotically regardless of the identification algorithm employed. These bounds demonstrate how the interplay between noise and time-variation fundamentally limits the accuracy of identification." @default.
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- W2734030405 date "1994-07-01" @default.
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- W2734030405 title "On the Worst-Case Identification of Slowly-Varying Systems" @default.
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- W2734030405 doi "https://doi.org/10.1016/s1474-6670(17)47747-0" @default.
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