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Book ChapterDOI

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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Journal ArticleDOI

Instrument Variables for Reducing Noise in Parallel MRI Reconstruction.

TL;DR: A new framework based on errors-in-variables (EIV) model is developed and provides possibilities that noiseless GRAPPA reconstruction could be achieved by existing methods that solve EIV problem other than IV method.
Journal ArticleDOI

Online Estimation of Ship's Mass and Center of Mass Using Inertial Measurements

TL;DR: In this article, a scale model of a ship in a wave basin is used for online estimation of mass and center of mass, and the experimental data is collected in free run experiments where the rudder angle was recorded and the ship's motion was measured using an inertial measurement unit.
Journal ArticleDOI

Nonlinear identification and optimal feedforward friction compensation for a motion platform

TL;DR: This study first identified the nonlinear dynamics of the platform using Higher Order Sinusoidal Input Describing Function based system identification, and modeled the friction using the Stribeck model and identified its parameters through a procedure including a special reference signal and the Nelder–Mead algorithm.
Proceedings ArticleDOI

On estimating initial conditions in unstructured models

TL;DR: This work proposes an approach that uses all the available data, and estimates also the initial conditions of the system, and shows how this approach can be applied to two methods in a beneficial manner.
Proceedings ArticleDOI

Real-time identification of linear continuous-time systems with slowly time-varying parameters

TL;DR: This study presents a real-time instrumental variable technique where linear filters are used to handle the time derivatives and the parameter variations are represented by a stochastic model.
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.
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

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

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.