The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Quantifying the error in estimated transfer functions with application to model order selection
Abstract
Previous results on estimating errors or error bounds on identified transfer functions have relied on prior assumptions about the noise and the unmodeled dynamics. This prior information took the form of parameterized bounding functions or parameterized probability density functions, in the time or frequency domain with known parameters. It is shown that the parameters that quantify this prior information can themselves be estimated from the data using a maximum likelihood technique. This significantly reduces the prior information required to estimate transfer function error bounds. The authors illustrate the usefulness of the method with a number of simulation examples. How the obtained error bounds can be used for intelligent model-order selection that takes into account both measurement noise and under-modeling is shown. Another simulation study compares the method to Akaike's well-known FPE and AIC criteria.>
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
System Identification: Theory for the User
Lennart Ljung, A. (1987) · Cited by 17,690
Asymptotic variance expressions for identified black-box transfer function models
Ljung, L. (1985) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1985.1104093 · Cited by 236
Hard frequency-domain model error bounds from least-squares like identification techniques
Wahlberg, B., & Ljung, L. (1992) · IEEE Transactions on Automatic Control · doi:10.1109/9.148343 · Cited by 197
Identification of Systems with Parametric and Nonparametric Uncertainty
Kosut, R. L., Lau, M. K., & Boyd, S. (1990) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc.1990.4791159 · Cited by 140
Goodwin, G. C., & Salgado, M. E. (1989) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.4480030405 · Cited by 131
Self-citation
Cited by
Model predictive control: past, present and future
Morari, M., & H. Lee, J. (1999) · Computers & Chemical Engineering · doi:10.1016/s0098-1354(98)00301-9 · Cited by 2,335
Kernel methods in system identification, machine learning and function estimation: A survey
Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., & Ljung, L. (2014) · Automatica · doi:10.1016/j.automatica.2014.01.001 · Cited by 741
Perspectives on system identification
Ljung, L. (2010) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2009.12.001 · Cited by 707
From experiment design to closed-loop control
Hjalmarsson, H. (2005) · Automatica · doi:10.1016/j.automatica.2004.11.021 · Cited by 506
On the estimation of transfer functions, regularizations and Gaussian processes—Revisited
Chen, T., Ohlsson, H., & Ljung, L. (2012) · Automatica · doi:10.1016/j.automatica.2012.05.026 · Cited by 451
Metadata
- Authors
- Goodwin, G. C., Gevers, M., & Ninness, B.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1992-07
- Volume
- 37
- Issue
- 7
- Pages
- 913-928
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2159425692
- Cited by
- 365
- References
- 5
- FWCI
- 43.02
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Quantifying the error in estimated transfer functions with application to model order selection. https://app.cochat.ai/papers/10.1109/9.148344
- The paper
- Goodwin, G. C., Gevers, M., & Ninness, B. (1992). Quantifying the error in estimated transfer functions with application to model order selection. IEEE Transactions on Automatic Control. https://doi.org/10.1109/9.148344
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
