Journal Article10.1016/0005-1098(94)90230-5
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
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TL;DR: Two new N4SID algorithms to identify mixed deterministic-stochastic systems are derived and these new algorithms are compared with existing subspace algorithms in theory and in practice.
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About: This article is published in Automatica. The article was published on 03 Jan 1994. The article focuses on the topics: Probabilistic analysis of algorithms & State space.
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Citations
OKID via Output Residuals: A Converter from Stochastic to Deterministic System Identification
TL;DR: This paper presents a new approach for the identification of linear state-space models from input–output measurements in the presence of noise, which uses the estimated observer output residuals to convert a stochastic identification problem into a virtually deterministic one.
14
Subspace based system identification with periodic excitation signals
Tomas McKelvey,Hüseyin Akçay +1 more
- 27 Dec 1995
TL;DR: An identification algorithm for use with data generated by periodic inputs is presented and it is shown that 2n + 1 noise-free output measurements are required to identify an nth order system.
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Fast CSF MRI for brain segmentation; Cross-validation by comparison with 3D T1-based brain segmentation methods
Lisa A. van der Kleij,Jeroen de Bresser,Jeroen Hendrikse,Jeroen C.W. Siero,Esben Thade Petersen,Esben Thade Petersen,Jill B. De Vis +6 more
TL;DR: Both CSF MRI sequences have a precision comparable to, and a very good correlation with established 3D T1-based automated segmentations methods for the segmentation of BPV and ICV, however, the short imaging time of the fast CSFMRI sequence is superior to the 3d T1 sequence on which segmentation with established methods is performed.
Fast subspace-based system identification
Young Man Cho,Thomas Kailath +1 more
TL;DR: A new implementation of the existing 4SID is proposed, which reduces the computational burden to O(NM) by exploiting the displacement and low-rank structure of the matrices.
14
Cointegration analysis with state space models
TL;DR: In this article, the results developed for cointegration analysis with state space models by Bauer and Wagner in a series of papers are presented and exemplified for empirical applications, and a canonical representation is developed and thereafter some available statistical results are briefly discussed.
14
References
•Book
System Identification: Theory for the User
Lennart Ljung
- 01 Jan 1987
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
•Book
Probability, random variables and stochastic processes
Athanasios Papoulis
- 01 Jan 1965
TL;DR: This chapter discusses the concept of a Random Variable, the meaning of Probability, and the axioms of probability in terms of Markov Chains and Queueing Theory.
•Book
Computer-Controlled Systems: Theory and Design
Karl Johan Åström,Björn Wittenmark +1 more
- 01 Jan 1984
TL;DR: This volume focuses on the design of computer-controlled systems, featuring computational tools that can be applied directly and are explained with simple paper-and-pencil calculations.
3.8K
Linear systems
S.R. Liberty
- 01 Nov 1981
TL;DR: In this paper, the authors studied the effect of local derivatives on the detection of intensity edges in images, where the local difference of intensities is computed for each pixel in the image.
2.6K