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Stochastic theory of minimal realization
Abstract
In this paper it is shown that a natural representation of a state space is given by the predictor space, the linear space spanned by the predictors when the system is driven by a Gaussian white noise input with unit covariance matrix. A minimal realization corresponds to a selection of a basis of this predictor space. Based on this interpretation, a unifying view of hitherto proposed algorithmically defined minimal realizations is developed. A natural minimal partial realization is also obtained with the aid of this interpretation.
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Cites
Observers for multivariable systems
Luenberger, D. (1966) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1966.1098323 · Cited by 1,763
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A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Self-citationThe Statistical Theory of Linear Systems
Cryer, J. D. (1990) · Technometrics · doi:10.1080/00401706.1990.10484617 · Cited by 745
Canonical variate analysis in identification, filtering, and adaptive control
Larimore, W. E. (1990) · 29th IEEE Conference on Decision and Control · doi:10.1109/cdc.1990.203665 · Cited by 573
Effects of Direct Social Experience on Trust Decisions and Neural Reward Circuitry
Fareri, D. S., Chang, L. J., & Delgado, M. R. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00148 · Cited by 147
Plant biodiversity of mountain grasslands as influenced by dairy farm management in the Eastern Alps
Pornaro, C., Spigarelli, C., Pasut, D., Ramanzin, M., Bovolenta, S., Sturaro, E., & Macolino, S. (2021) · Agriculture, Ecosystems & Environment · doi:10.1016/j.agee.2021.107583 · Cited by 17
Metadata
- Authors
- Akaike, H.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 1974-12
- Volume
- 19
- Issue
- 6
- Pages
- 667-674
- ISSN
- 0018-9286 (ISSN-L)
- OpenAlex
- W2006345955
- Cited by
- 429
- References
- 1
- FWCI
- 18.04
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
