Models for Robust Estimation and Identification

Shivkumar Chandrasekaran () and Keith Schubert ()
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Shivkumar Chandrasekaran: University of California, Department of Electrical and Computer Engineering
Keith Schubert: University of Redlands, Department of Mathematics and Computer Science

A chapter in Total Least Squares and Errors-in-Variables Modeling, 2002, pp 203-212 from Springer

Abstract: Abstract In this paper, we will investigate estimation and identification theories with the goal of determining some new methods of adding robustness. We consider uncertain estimation problems, namely ones in which the uncertainty multiplies the quantities to be estimated. Mathematically the problem can be stated as, for system matrices and data matrices that lie in the sets (A + δA) and (b + δb) respectively, find the value of x that minimizes the cost ‖(A + δA)x − (b + δb)‖. We will examine how the proposed techniques compare with currently used methods such as Least Squares (LS), Total Least Squares (TLS), and Tikhonov Regularization (TR). Several results are presented and some future directions are suggested.

Keywords: regularizaion; least squares. (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-017-3552-0_18

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DOI: 10.1007/978-94-017-3552-0_18

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