Estimation of Large-Scale Implicit Models Using 2-Stage Methods
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 subsequen...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Norwegian Society of Automatic Control
1985-01-01
|
| Series: | Modeling, Identification and Control |
| Subjects: | |
| Online Access: | http://www.mic-journal.no/PDF/1985/MIC-1985-1-1.pdf |
| Summary: | 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. |
|---|---|
| ISSN: | 0332-7353 1890-1328 |