Journal ArticleDOI
Subspace algorithms for the stochastic identification problem
Peter Van Overschee,Bart De Moor +1 more
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TLDR
A new subspace algorithm is derived to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semi-infinite block Hankel matrices.About:
This article is published in Automatica.The article was published on 1993-05-20. It has received 480 citations till now. The article focuses on the topics: Singular value decomposition & Hankel matrix.read more
Citations
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Journal ArticleDOI
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
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.
Book
Inference in Hidden Markov Models
TL;DR: This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory, and builds on recent developments to present a self-contained view.
Journal ArticleDOI
Machine Recognition of Human Activities: A Survey
TL;DR: A comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications is presented.
Journal ArticleDOI
Reference-based stochastic subspace identification for output-only modal analysis
Bart Peeters,Guido De Roeck +1 more
TL;DR: In this paper, a novel approach of stochastic subspace identification is presented that incorporates the idea of the reference sensors already in the identification step: the row space of future outputs is projected into the rowspace of past reference outputs.
Journal ArticleDOI
Dynamic Textures
TL;DR: A characterization of dynamic textures that poses the problems of modeling, learning, recognizing and synthesizing dynamic textures on a firm analytical footing and experimental evidence that, within the framework, even low-dimensional models can capture very complex visual phenomena is presented.
References
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Book
System Identification: Theory for the User
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 ChapterDOI
Relations Between Two Sets of Variates
TL;DR: The concept of correlation and regression may be applied not only to ordinary one-dimensional variates but also to variates of two or more dimensions as discussed by the authors, where the correlation of the horizontal components is ordinarily discussed, whereas the complex consisting of horizontal and vertical deviations may be even more interesting.
Proceedings ArticleDOI
Canonical variate analysis in identification, filtering, and adaptive control
TL;DR: The canonical variate analysis (CVA) approach as discussed by the authors is an approach for system identification, filtering, and adaptive control that is characterized by a generalized singular value decomposition (SVD).
Related Papers (5)
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
Subspace Identification for Linear Systems: Theory - Implementation - Applications
Peter Van Overschee,Bart De Moor +1 more