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Data Driven Model Learning for Engineers: With Applications to Univariate Time Series

Mercere, Guillaume

2023

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Combining Linear Algebra and Numerical Optimization for Gray-Box Affine State-Space Model Identification

IEEE transactions on automatic control, 2020-08, Vol.65 (8), p.3272-3285

2020

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Parameterization and identification of multivariable state-space systems: A canonical approach

Automatica (Oxford), 2011-08, Vol.47 (8), p.1547-1555

2011

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Realization Theory for LPV State-Space Representations With Affine Dependence

IEEE transactions on automatic control, 2017-09, Vol.62 (9), p.4667-4674

2017

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Subspace identification with constraints on the impulse response

International journal of control, 2017-08, Vol.90 (8), p.1728-1735

2017

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Data enabled Predictive Control of LPV systems

Control engineering practice, 2024-08, Vol.149, p.105969, Article 105969

2024

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MIMO system identification using common denominator and numerators with known degrees

International journal of adaptive control and signal processing, 2022-04, Vol.36 (4), p.870-881

2022

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LPV-ARX representations of LPV state-space models with affine dependence

Systems & control letters, 2023-03, Vol.173, p.105459, Article 105459

2023

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Model learning of the tire–road friction slip dependency under standard driving conditions

Control engineering practice, 2022-04, Vol.121, p.105048, Article 105048

2022

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New developments for experimental modal analysis of aircraft structures

MATEC Web of Conferences, 2015, Vol.20, p.1001

2015

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Identifiability of affine linear parameter-varying models

Automatica (Oxford), 2017-06, Vol.80, p.62-74

2017

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Active control of road vehicle’s drag for varying upstream flow conditions using a recursive subspace based predictive control methodology

Control engineering practice, 2024-11, Vol.152, p.106071, Article 106071

2024

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Active control of road vehicle’s drag for varying upstream flow conditions using a recursive subspace based predictive control methodology

Control engineering practice, 2024-11, Vol.152

2024

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A Robust and Regularized Algorithm for Recursive Total Least Squares Estimation

IEEE control systems letters, 2024-01, Vol.8, p.1006-1011

2024

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Optimal identification experiment design for LPV systems using the local approach

Automatica (Oxford), 2018-01, Vol.87, p.258-266

2018

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Continuous-Time Linear Parameter-Varying Identification of a Cross Flow Heat Exchanger: A Local Approach

IEEE transactions on control systems technology, 2011-01, Vol.19 (1), p.64-76

2011

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Noise covariance matrix estimation with subspace model identification for Kalman filtering

International journal of adaptive control and signal processing, 2021-04, Vol.35 (4), p.591-611

2021

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Dynamic models for bird population—A parameter-varying partial differential equation identification approach

Control engineering practice, 2019-10, Vol.91, p.104091, Article 104091

2019

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Tracking Distributed Parameters System Dynamics with Recursive Dynamic Mode Decomposition with Control

SIAM journal on applied dynamical systems, 2023-03, Vol.22 (1), p.37-64

2023

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Phase estimation of a 2D fringe pattern using a monogenic-based multiscale analysis

Journal of the Optical Society of America. A, Optics, image science, and vision, 2019-11, Vol.36 (11), p.C143

2019

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New developments for matrix fraction descriptions: A fully-parametrised approach

Automatica (Oxford), 2016-04, Vol.66, p.15-24

2016

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A stochastic subspace system identification algorithm for state-space systems in the general 2-D Roesser model form

International journal of control, 2018-12, Vol.91 (12), p.2743-2771

2018

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Subspace Identification of 2-D CRSD Roesser Models With Deterministic-Stochastic Inputs: A State Computation Approach

IEEE transactions on control systems technology, 2017-05, Vol.25 (3), p.1108-1115

2017

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Enhanced Recursive Total Least Squares Method With Subspace Tracking and Noise Covariance Estimation

IEEE control systems letters, 2024-01, Vol.8, p.1114-1119

2024

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Minimal Realizations of Input-Output Behaviors by LPV State-Space Representations with Affine Dependence

IEEE control systems letters, 2023-01, Vol.7, p.1-1

2023

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Data Driven Model Learning for Engineers: With Applications to Univariate Time Series

Combining Linear Algebra and Numerical Optimization for Gray-Box Affine State-Space Model Identification

Parameterization and identification of multivariable state-space systems: A canonical approach

Realization Theory for LPV State-Space Representations With Affine Dependence

Subspace identification with constraints on the impulse response

Data enabled Predictive Control of LPV systems

MIMO system identification using common denominator and numerators with known degrees

LPV-ARX representations of LPV state-space models with affine dependence

Model learning of the tire–road friction slip dependency under standard driving conditions

New developments for experimental modal analysis of aircraft structures

Identifiability of affine linear parameter-varying models

Active control of road vehicle’s drag for varying upstream flow conditions using a recursive subspace based predictive control methodology

Active control of road vehicle’s drag for varying upstream flow conditions using a recursive subspace based predictive control methodology

A Robust and Regularized Algorithm for Recursive Total Least Squares Estimation

Optimal identification experiment design for LPV systems using the local approach

Continuous-Time Linear Parameter-Varying Identification of a Cross Flow Heat Exchanger: A Local Approach

Noise covariance matrix estimation with subspace model identification for Kalman filtering

Dynamic models for bird population—A parameter-varying partial differential equation identification approach

Tracking Distributed Parameters System Dynamics with Recursive Dynamic Mode Decomposition with Control

Phase estimation of a 2D fringe pattern using a monogenic-based multiscale analysis

New developments for matrix fraction descriptions: A fully-parametrised approach

A stochastic subspace system identification algorithm for state-space systems in the general 2-D Roesser model form

Subspace Identification of 2-D CRSD Roesser Models With Deterministic-Stochastic Inputs: A State Computation Approach

Enhanced Recursive Total Least Squares Method With Subspace Tracking and Noise Covariance Estimation

Minimal Realizations of Input-Output Behaviors by LPV State-Space Representations with Affine Dependence

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