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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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Book

Process Modelling, Identification, and Control

TL;DR: The control problem and Design of Simple Controllers is studied and the design of simple controllers is designed to achieve optimal process control.
Journal ArticleDOI

Control of Towing Kites for Seagoing Vessels

TL;DR: The basic features of the flight control of the SkySails towing kite system are presented and the generation of dynamical flight patterns are explained.
Journal ArticleDOI

Rapid online selection between multiple motor plans.

TL;DR: Online decisional processing in humans is examined by asking them to make reaching movements with obstacles located just to the sides of a direct path between start and end targets to show that decisional processes are rapidly implemented during movement execution.
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Forecasting Trajectory and Behavior of Road-Agents Using Spectral Clustering in Graph-LSTMs

TL;DR: A novel approach for traffic forecasting in urban traffic scenarios using a combination of spectral graph analysis and deep learning that reduces the average prediction error by approximately 75% over prior algorithms and achieves a weighted average accuracy of 91.2% for behavior prediction.
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Robust weighted fusion Kalman filters for multisensor time-varying systems with uncertain noise variances

TL;DR: This paper addresses the design of robust weighted fusion Kalman filters for multisensor time-varying systems with uncertainties of noise variances using the minimax robust estimation principle and the unbiased linear minimum variance (ULMV) optimal estimation rule.
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.
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.
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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.