| 1. |
- Ljung, Stefan, et al.
(författare)
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Error Propagation Properties of Recursive Least Squares Adaptation Algorithms
- 1985
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 21:2, s. 157-167
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Tidskriftsartikel (refereegranskat)abstract
- The numerical properties of implementations of the recursive least-squares identification algorithm are of great importance for their continuous use in various adaptive schemes. Here we investigate how an error that is introduced at an arbitrary point in the algorithm propagates. It is shown that conventional LS algorithms, including Bierman's UD-factorization algorithm are exponentially stable with respect to such errors, i.e. the effect of the error decays exponentially. The base of the decay is equal to the forgetting factor. The same is true for fast lattice algorithms. The fast least-squares algorithm, sometimes known as the ‘fast Kalman algorithm’ is however shown to be unstable with respect to such errors.
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| 2. |
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| 3. |
- Fnaiech, Farhat, et al.
(författare)
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Recursive Identification of Bilinear Systems
- 1985
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Methods of identifying bilinear systems from recorded input-output data are discussed in this article. A short survey of the existing literature on the topic is given. ‘Standard’ methods from linear systems identification, such as least squares, extended least squares, recursive prediction error and instrumental variable methods are transferred to bilinear, input-output model structures and tested in simulation. Special attention is paid to problems of stabilizing the model predictor, and it is shown how a time-varying Kalman filter and associated parameter estimation algorithm can deal with this problem.
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| 4. |
- Fnaiech, Farhat, et al.
(författare)
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Recursive Identification of Bilinear Systems
- 1987
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Ingår i: International Journal of Control. - : Taylor & Francis. - 0020-7179 .- 1366-5820. ; 45:2, s. 453-470
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Tidskriftsartikel (refereegranskat)abstract
- Methods of identifying bilinear systems from recorded input-output data are discussed in this article. A short survey of the existing literature on the topic is given. ‘Standard’ methods from linear systems identification, such as least squares, extended least squares, recursive prediction error and instrumental variable methods are transferred to bilinear, input-output model structures and tested in simulation. Special attention is paid to problems of stabilizing the model predictor, and it is shown how a time-varying Kalman filter and associated parameter estimation algorithm can deal with this problem.
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| 5. |
- Forsman, Krister, et al.
(författare)
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Merging 'Reasoning' and Filtering in a Bayesian Framework : Some Sensitivity and Optimality Aspects
- 1989
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Ingår i: Proceedings of the 28th Conference on Decision and Control. - Linköping : Linköping University. ; , s. 1427-1429
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Konferensbidrag (refereegranskat)abstract
- It is shown how to incorporate symbolic or logical knowledge into a conventional framework of noisy observations in dynamical systems. The idea is based on approximating the optimal solution that could, theoretically, be computed if a complete Bayesian framework were known (and infinite computational power were available). The nature of the approximations, the deviations from optimality and the sensitivity to ad hoc parameters are specifically addressed.
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| 6. |
- Gevers, Michel, et al.
(författare)
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Benefits of Feedback in Experiment Design
- 1985
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Ingår i: Proceedings of the 7th IFAC Symposium on Identification and System Parameter Estimation. - : Pergamon Press. ; , s. 909-914
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Konferensbidrag (refereegranskat)
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| 7. |
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| 8. |
- Gevers, Michel, et al.
(författare)
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Optimal Experimental Designs with Respect to the Intended Model Application
- 1986
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Ingår i: Automatica. - : Elsevier. - 0005-1098 .- 1873-2836. ; 22:5, s. 543-554
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Tidskriftsartikel (refereegranskat)abstract
- The purpose of the design of identification experiments is to make the collected data maximally informative with respect to the intended use of the model, subject to constraints that might be at hand. When the true system is replaced by an estimated model, there results a performance degradation that is due to the error in the transfer function estimates. Using some recent asymptotic expressions for the bias and the variance of the estimated transfer function, it is shown how this performance degradation can be minimized by a proper experiment design. Several applications, where it is beneficial to let the experiment be carried out in closed loop, are highlighted.
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| 9. |
- Gevers, Michel, et al.
(författare)
-
Optimal Experimental Designs with Respect to the Intended Model Application
- 1985
-
Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The purpose of the design of identification experiments is to make the collected data maximally informative with respect to the intended use of the model, subject to constraints that might be at hand. When the true system is replaced by an estimated model, there results a performance degradation that is due to the error in the transfer function estimates. Using some recent asymptotic expressions for the bias and the variance of the estimated transfer function, it is shown how this performance degradation can be minimized by a proper experiment design. Several applications, where it is beneficial to let the experiment be carried out in closed loop, are highlighted.
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| 10. |
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