Suboptimal algorithms for worst case identification in H∞ and model validation

New algorithms based on convex programming are proposed for worst case system identification. The algorithms are optimal within a factor of two asymptotically. Further, model validation, or data consistency, is embedded in the identification process. Explicit worst case identification error bounds in the H/sup /spl infin// norm are also derived for both uniformly and nonuniformly spaced frequency response samples. >

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