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This book applies functional analysis and complex analysis to problems of interpolation in spaces of analytic functions. It examines problems of recovery--producing approximations to functions from measured values. These values may in turn be corrupted by small errors; the book discusses methods for producing good models using this partial and inaccurate information. The practical applications include systems identification, signal processing, and sampling. The book provides mathematical introductions to many important areas of current research, including H( control theory, sampling and systems processing, and the theory of worst-case identification. This is the first formal treatment of worst-case identification, a field where the author is a leading authority, and the discussion includes much practical material on input design and identification algorithms. This material is appropriate for a graduate-level course on function spaces and the operators acting on them.
This text investigates the application of functional and complex analysis to the mathematical challenges of interpolation, function recovery, and systems identification. Jonathan R. Partington, a recognized authority in the field, utilizes a rigorous mathematical framework to address how one might construct accurate models from partial or corrupted data. The book bridges the gap between theoretical function spaces and practical engineering applications, providing a systematic approach to worst-case identification and signal processing.
What You Will Find
Experts identify this monograph as a foundational text for graduate-level study in operator theory and its engineering applications. Readers frequently note the high level of academic density, which requires a strong background in functional analysis to fully grasp the presented proofs and algorithms.
Page Count:
288
Publication Date:
1997-10-09
Publisher:
Clarendon Press
ISBN-10:
0198500246
ISBN-13:
9780198500247
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