The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Constructing NARMAX models using ARMAX models
Abstract
This paper outlines how it is possible to decompose a complex non-linear modelling problem into a set of simpler linear modelling problems. Local ARMAX models valid within certain operating regimes are interpolated to construct a global NARMAX (non-linear NARMAX) model. Knowledge of the system behaviour in terms of operating regimes is the primary basis for building such models, hence it should not be considered as a pure black-box approach, but as an approach that utilizes a limited amount of a priori system knowledge. It is shown that a large class of non-linear systems can be modelled in this way, and indicated how to decompose the systems range of operation into operating regimes. Standard system identification algorithms can be used to identify the NARMAX model, and several aspects of the system identification problem are discussed and illustrated by a simulation example.
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Gill, P. E., Murray, W., & Wright, M. H. (2019) · doi:10.1137/1.9781611975604 · Cited by 1,737
Cited by
Survey of gain-scheduling analysis and design
Leith, D. J., & Leithead, W. E. (2000) · International Journal of Control · doi:10.1080/002071700411304 · Cited by 939
Metadata
- Authors
- JOHANSEN, T. A., & FOSS, B.
- Venue
- International Journal of Control
- Publisher
- Informa UK Limited
- Published
- 1993-11
- Volume
- 58
- Issue
- 5
- Pages
- 1125-1153
- ISSN
- 0020-7179 (ISSN-L)
- 1366-5820
- OpenAlex
- W2021803428
- Cited by
- 363
- References
- 1
- FWCI
- 15.41
- Type
- journal article
- Language
- en
- Open access
- closed
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- JOHANSEN, T. A., & FOSS, B. (1993). Constructing NARMAX models using ARMAX models. International Journal of Control. https://doi.org/10.1080/00207179308923046
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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.