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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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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

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.
Journal ArticleDOI

A new kernel-based approach for linear system identification

TL;DR: A new kernel-based approach for linear system identification of stable systems that model the impulse response as the realization of a Gaussian process whose statistics include information not only on smoothness but also on BIBO-stability.
Journal ArticleDOI

Zebedee: Design of a Spring-Mounted 3-D Range Sensor with Application to Mobile Mapping

TL;DR: The results demonstrate that the six-degree-of-freedom trajectory of a passive spring-mounted range sensor can be accurately estimated from laser range data and industrial-grade inertial measurements in real time and that a quality 3-D point cloud map can be generated concurrently using the same data.
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A data-driven Koopman model predictive control framework for nonlinear flows

TL;DR: In this article, the authors propose a model predictive control framework for closed-loop feedback control of nonlinear flows in a fully data-driven and model-free manner, which is based on the Koopman operator theory.
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Pupil-linked arousal modulates behavior in rats performing a whisker deflection direction discrimination task.

TL;DR: A tight correlation between pupil dynamics, perceptual performance, and reaction time in behaving rats, all of which are modulated by fluctuating arousal state is suggested.
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Long-term structural health monitoring of the Cleddau bridge: evaluation of quasi-static temperature effects on bearing movements

TL;DR: In this paper, the Cleddau bridge was used to investigate thermal effects in steel box-girder bridges and their bearings, and a physics-based model of the bridge is created to which temperature distributions inferred from in situ measurements are supplied as input.
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Vehicle model identification by integrated prediction error minimization

TL;DR: This work calibrates models of odometry, powertrain dynamics, and wheel slip as it affects body frame velocity and demonstrates its usefulness for the calibration of wheeled vehicle models used in control and estimation.
Journal ArticleDOI

Modeling and control of tubular solid-oxide fuel cell systems. I: Physical models and linear model reduction

TL;DR: In this paper, the authors describe the development of a transient model of an anode-supported, tubular solid-oxide fuel cell (SOFC), which is designed with process-control applications in mind, although it can certainly be applied more widely.