Title :
Suboptimal algorithms for worst case identification and model validation
Author_Institution :
Dept. of Electr. & Comput. Eng., Louisiana State Univ., Baton Rouge, LA, USA
Abstract :
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∞ norm are also derived for both uniformly and nonuniformly spaced frequency response samples
Keywords :
approximation theory; computational complexity; convex programming; frequency response; identification; approximation theory; computational complexity; convex programming; data consistency; error bounds; frequency response samples; model validation; suboptimal algorithms; system identification; worst case identification; Computer aided software engineering; Costs; Error correction; Frequency response; H infinity control; Interpolation; Jacobian matrices; Noise level; System identification; Transfer functions;
Conference_Titel :
Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-1298-8
DOI :
10.1109/CDC.1993.325085
