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An LFT approach to parameter estimation
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DOI：10.1016/j.automatica.2008.04.026.png)
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In this paper we consider a unified framework for parameter estimation problems. Under this framework, the unknown parameters appear in a linear fractional transformation (LIFT). A key advantage of the LIFT problem formulation is that it allows us to efficiently compute gradients, Hessians, and Gauss-Newton directions for general parameter estimation problems without resorting to inefficient finite-difference approximations. The generality of this approach also allows us to consider issues such as identifiability, persistence of excitation, and convergence for a large class of model structures under a single unified framework. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
System identification
Parameter estimation
Linear fractional transformation
Maximum likelihood
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IF:
5.9
论文数:
1.2W
被引数:
5.2W
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An analysis of the parametrization by data driven local coordinates for multivariable linear systems.png)
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