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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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Citations
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Journal ArticleDOI

Acoustic thoracic image of crackle sounds using linear and nonlinear processing techniques.

TL;DR: It is concluded that a confident image of crackle sounds distribution by crackles counting using TVAR on the thoracic surface is thoroughly possible and might represent an aid to the clinical evaluation of pulmonary diseases that produce this sort of adventitious discontinuous lung sounds.
Journal ArticleDOI

Review: Recent progress on variable projection methods for structured low-rank approximation

TL;DR: This paper gives an overview of recent progress in efficient local optimization algorithms for solving weighted mosaic-Hankel structured low-rank approximation problems.
Journal ArticleDOI

Nonlinear System Identification of Neural Systems from Neurophysiological Signals.

TL;DR: It is argued that nonlinear modelling and analysis are necessary to study neuronal processing and signal transfer in neural systems quantitatively and have the potential to produce sensitive biomarkers to facilitate the development of precision diagnostic tools for evaluating neurological disorders and the effects of targeted intervention.
Proceedings Article

NoRML: No-Reward Meta Learning

TL;DR: In this article, the authors introduce a meta-learning approach called No-Reward Meta Learning (NoRML), which allows for self-adaptation of learned policies by using observable dynamics of the environment instead of an explicit reward function in the finetune step.
Proceedings ArticleDOI

Self-Aware Cyber-Physical Systems-on-Chip

TL;DR: The Cyber-Physical System-on-Chip (CPSoC) concept is presented as an exemplar of a self-aware SoC that intrinsically couples on-chip and cross-layer sensing and actuation using a sensor-actuator rich fabric to enable self-awareness.
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.