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 Article
Embedded Model Control urges disturbance modelling and rejection
Enrico Canuto,Wilber Acuna-Bravo,Andrés Molano-Jimenez,Jose Alejandro Ospina,Carlos Perez-Montenegro +4 more
TL;DR: In this paper, the model-based control law must and can be kept intact under uncertainty, if the controllable dynamics is complemented by a suitable disturbance dynamics capable of real-time encoding the different uncertainties affecting the embedded model.
Proceedings ArticleDOI
Experimental identification of the small turbojet engine MPM-20
TL;DR: Modelling of a small turbojet engine MPM-20 through programming environment Matlab and Simulink extension and methods of experimental identification along with their application to the measured engine parameter data obtained by testing in the Laboratory of Intelligent Control Systems of Jet Engines.
Proceedings ArticleDOI
Bayesian Kernel-Based Linear Control Design
TL;DR: The posterior distribution of the impulse response available from the Bayesian framework is exploited to perform control design using three different approaches; one of these is the minimization of the expected (posterior) distance from the desired closed loop system.
Journal ArticleDOI
Investigation on dynamic behaviour of a full-scale reinforced concrete bridge subjected to strong earthquakes using an automated analysis platform
TL;DR: A dynamic performance index based on an AutoRegressive Moving Average with eXogenous excitation (ARMAX) model utilises the response predicted from an ARMAX model to evaluate bridge behaviour during strong earthquakes shows that the bridge did not follow linear behaviour during the two strong earthquakes as expected from a linear system.
Posted Content
Identifiable paths and cycles in linear compartmental models
TL;DR: A class of linear compartmental models which have the property that all of the monomial functions of parameters associated to the directed cycles and paths from input compartments to output compartments are identifiable and give sufficient conditions to obtain an identifiable path/cycle model are introduced.
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.