Article ID Journal Published Year Pages File Type
696148 Automatica 2013 6 Pages PDF
Abstract

Three different classes of bias-compensating least squares identification methods are compared, and shown to be identical. It is also discussed how the user parameters in the classes can be chosen to achieve optimal accuracy of the parameter estimates.

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Physical Sciences and Engineering Engineering Control and Systems Engineering
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Comparing some classes of bias-compensating least squares methods
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