“Estimation of Large-Scale Implicit Models Using 2-Stage Methods”
Authors: Rolf Henriksen,Affiliation: NTNU, Department of Engineering Cybernetics
Reference: 1985, Vol 6, No 1, pp. 3-19.
Keywords: Parameter estimation, large scale systems, two-stage methods, decentralized filtering, prediction error methods, implicit models
Abstract: The problem of estimating large scale implicit (non-recursive) models by two- stage methods is considered. The first stage of the methods is used to construct or estimate an explicit form of the total model, by constructing a minimal stochastic realization of the system. This model is then subsequently used in the second stage to generate instrumental variables for the purpose of estimating each sub-model separately. This latter stage can be carried out by utilizing a generalized least squares method, but most emphasis is put on utilizing decentralized filtering algorithms and a prediction error formulation. A note about the connection between the original TSLS-method (two-stage least squares method) and stochastic realization is also made.
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DOI: 10.4173/mic.1985.1.1DOI forward links to this article:
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| [2] D. Di Ruscio, R. Henriksen and J.G. Balchen (1993), doi:10.1109/CDC.1993.325869 |
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BibTeX:
@article{MIC-1985-1-1,
title={{Estimation of Large-Scale Implicit Models Using 2-Stage Methods}},
author={Henriksen, Rolf},
journal={Modeling, Identification and Control},
volume={6},
number={1},
pages={3--19},
year={1985},
doi={10.4173/mic.1985.1.1},
publisher={Norwegian Society of Automatic Control}
};