Solving Linear Problems in the Presence of Bounded Data Perturbations

B. Z. Kacewicz
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B. Z. Kacewicz: University of Warsaw, Institute of Applied Mathematics

Chapter 3 in Bounding Approaches to System Identification, 1996, pp 29-42 from Springer

Abstract: Abstract In most computational problems of engineering or numerical analysis available input data (information) is not exact. Perturbations in data may arise for instance from measurement or round-off errors, to mention only these two possible sources. The problem of how inaccuracy in data influences results (for instance, how does it affect a quality of system identification or signal recovery) attracts attention not only for obvious practical reasons, but also motivates a number of theoretical papers. For example, since a long time the case of stochastic errors in information has been studied by statisticians, to mention only the monograph by Wahba,(1) where extensive references to the subject can be found. On the other hand, an active stream of research is based on deterministic assumptions about the noise. Such assumptions are imposed when no appropriate statistical knowledge about the behavior of data errors is available, or simply when statistical analysis is not of interest. The assumption often made in this framework is that errors in information are unknown but bounded. Among many other papers, the bounding approach is discussed in Refs. 2–5.

Keywords: Linear Problem; Minimal Error; Minimal Diameter; Signal Recovery; Linear Normed Space (search for similar items in EconPapers)
Date: 1996
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DOI: 10.1007/978-1-4757-9545-5_3

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