Analysis of Linear Methods for Robust Identification in ℓ 1

Abstract Worst-case analysis of system identification by means of the linear algorithms such as least squares is considered. Estimates for worst-case and average errors are provided, showing that worst-case robust convergence cannot occur in the l 1 identification problem. The case of periodic inputs is also analysed. Finally pseu-dorandomness assumptions are introduced which allow more powerful convergence results in a deterministic framework.

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