Book ChapterDOI
System Identification I
Biao Huang,Yutong Qi,Akm Monjur Murshed +2 more
- pp 31-56
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The article was published on 2012-12-11. It has received 1704 citations till now. The article focuses on the topics: Nonlinear system identification & System identification.read more
Citations
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Proceedings ArticleDOI
User friendly Box-Jenkins identification using nonparametric noise models
TL;DR: The identification of SISO linear dynamic systems in the presence of output noise disturbances is considered and a ‘nonparametric’ Box-Jenkins approach is studied: the parametric noise model is replaced by a nonparametric model that is obtained in a preprocessing step, and this without any user interaction.
Journal ArticleDOI
Analysis of a nonsmooth optimization approach to robust estimation
Laurent Bako,Henrik Ohlsson +1 more
TL;DR: It is shown that under appropriate conditions on the data, an exact estimate can be recovered from data corrupted by a large (even infinite) number of gross errors.
Journal ArticleDOI
Parameter estimation in time-triggered and event-triggered model-based control of uncertain systems
Eloy Garcia,Panos J. Antsaklis +1 more
TL;DR: In this article on-line parameter estimation of dynamical systems is addressed in the context of model-based networked control systems (MB-NCSs) and stability conditions that are robust to parameter uncertainties and lack of feedback for extended intervals of time are presented.
Journal ArticleDOI
ALVEN: Algebraic learning via elastic net for static and dynamic nonlinear model identification
Weike Sun,Richard D. Braatz +1 more
TL;DR: An algorithm is proposed that combines nonlinear feature generation and sparse regression to learn interpretable nonlinear models from noisy and limited data, called ALVEN, to produce an interpretable model useful for process applications while avoiding overfitting.
Proceedings ArticleDOI
Model reduction by moment matching for linear switched systems
TL;DR: A moment-matching method for the model reduction of linear switched systems (LSSs) based upon a partial realization theory of LSSs and it is similar to the Krylov subspace methods used for moment matching for linear systems.
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.
Journal ArticleDOI
Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
Journal ArticleDOI
Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
Milan Korda,Igor Mezic +1 more
TL;DR: This work extends the Koopman operator to controlled dynamical systems and applies the Extended Dynamic Mode Decomposition (EDMD) to compute a finite-dimensional approximation of the operator in such a way that this approximation has the form of a linearcontrolled dynamical system.
Journal ArticleDOI
A Tour of Reinforcement Learning: The View from Continuous Control
TL;DR: The authors surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications, and reviews the general formulation, terminology, and techniques for reinforcement learning for continuous control.
Journal ArticleDOI
SPICE: A Sparse Covariance-Based Estimation Method for Array Processing
Petre Stoica,Prabhu Babu,Jian Li +2 more
TL;DR: This paper presents a novel SParse Iterative Covariance-based Estimation approach, abbreviated as SPICE, to array processing, obtained by the minimization of a covariance matrix fitting criterion and is particularly useful in many- snapshot cases but can be used even in single-snapshot situations.