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System Identification I

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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.

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Identification for advanced motion control : numerically reliable algorithms for complex systems

TL;DR: This thesis argues that numerical aspects of software development should be explicitly taken into account when aiming to develop software that performs well in practice.
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System Identification of Distributory Canals in the Indus Basin

TL;DR: In this article, the authors derived channel models for regulation in irrigation canals using system identification techniques, which are computationally inexpensive and accurate for simulating water profiles; thus qualifying for addressing efficient control design problems.
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A numerical investigation of direct and indirect closed-loop architectures for estimating nonminimum-phase zeros

TL;DR: A numerical investigation of three direct architectures and three indirect architectures for identifying a plant operating in closed loop is presented, to compare the accuracy of the NMP-zero estimates obtained from each method and for each architecture.
References
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Book

System Identification: Theory for the User

Lennart Ljung
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.
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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.
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Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control

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.
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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.
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SPICE: A Sparse Covariance-Based Estimation Method for Array Processing

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.