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Subspace methods for system identification
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In this article, the authors propose a formalization theory for the realization of deterministic problems in linear algebra and disctrete-time linear systems based on the Kalman filter.Abstract:
Introduction Part I: Preliminaries Linear Algebra and Preliminaries Disctrete-time Linear Systems Stochastic Processes Kalman Filter Part II: Realization Theory Realization of Deterministic Problems Stochastic Realization Theory I Stochastic Realization Theory II Part III: Subspace Identification Subspace Identification I: ORT Subspace Identification II: CCA Identification of Closed-loop System Appendices Least-squares Method Input Signals for System Identification Overlapping Parametrization Matlab(R) Programs Solutions to Problemsread more
Citations
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Closed-loop and activity-guided optogenetic control.
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A spectral algorithm for learning hidden Markov models
TL;DR: In this article, the authors prove that under a natural separation condition (bounds on the smallest singular value of the HMM parameters), there is an efficient and provably correct algorithm for learning hidden Markov models.
References
More filters
Journal ArticleDOI
From model-based control to data-driven control: Survey, classification and perspective
Zhongsheng Hou,Zhuo Wang +1 more
TL;DR: This paper is a brief survey on the existing problems and challenges inherent in model-based control (MBC) theory, and some important issues in the analysis and design of data-driven control (DDC) methods are here reviewed and addressed.
Journal ArticleDOI
An overview of subspace identification
TL;DR: This paper provides an overview of the state of the art of subspace identification methods for both open-loop and closed-loop systems.
Journal ArticleDOI
Data-Driven Model Predictive Control With Stability and Robustness Guarantees
TL;DR: The presented results provide the first (theoretical) analysis of closed-loop properties, resulting from a simple, purely data-driven MPC scheme, including a slack variable with regularization in the cost.
Journal ArticleDOI
Closed-loop and activity-guided optogenetic control.
TL;DR: This work highlights technical and theoretical foundations as well as recent advances and opportunities in optogenetic experimentation in the context of addressing these challenges with closed-loop optogenetics control in behaving animals.
Proceedings Article
A spectral algorithm for learning hidden Markov models
TL;DR: In this article, the authors prove that under a natural separation condition (bounds on the smallest singular value of the HMM parameters), there is an efficient and provably correct algorithm for learning hidden Markov models.
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