Suboptimal algorithms for worst case identification and model validation

摘要

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 H/sup /spl infin// norm are also derived for both uniformly and nonuniformly spaced frequency response samples.