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Modeling and identification of uncertain-input systems

Automatica (Oxford), 2019-07, Vol.105, p.130-141

2019

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Bayesian frequentist bounds for machine learning and system identification

Automatica (Oxford), 2022-12, Vol.146, p.110599, Article 110599

2022

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A Bayesian approach to sparse dynamic network identification

Automatica (Oxford), 2012-08, Vol.48 (8), p.1553-1565

2012

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Errors-in-variables identification in dynamic networks — Consistency results for an instrumental variable approach

Automatica (Oxford), 2015-12, Vol.62, p.39-50

2015

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Subsystem identification of multivariable feedback and feedforward systems

Automatica (Oxford), 2016-10, Vol.72, p.131-137

2016

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Hammerstein system identification using nuclear norm minimization

Automatica (Oxford), 2012-09, Vol.48 (9), p.2189-2193

2012

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Dual time–frequency domain system identification

Automatica (Oxford), 2012-12, Vol.48 (12), p.3031-3041

2012

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Blind system identification using precise and quantized observations

Automatica (Oxford), 2013-09, Vol.49 (9), p.2822-2830

2013

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Generic identifiability of subnetworks in a linear dynamic network: The full measurement case

Automatica (Oxford), 2022-03, Vol.137, p.110093, Article 110093

2022

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Can errors-in-variables systems be identified from closed-loop experiments?

Automatica (Oxford), 2013-02, Vol.49 (2), p.681-684

2013

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Optimal and two-step adaptive quantum detector tomography

Automatica (Oxford), 2022-07, Vol.141, p.110296, Article 110296

2022

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Kernel methods in system identification, machine learning and function estimation: A survey

Automatica (Oxford), 2014-03, Vol.50 (3), p.657-682

2014

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Convergence and consistency of recursive least squares with variable-rate forgetting

Automatica (Oxford), 2020-09, Vol.119, p.109052, Article 109052

2020

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System identification in a networked environment using second order statistical properties

Automatica (Oxford), 2013-02, Vol.49 (2), p.652-659

2013

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Robust MPC with recursive model update

Automatica (Oxford), 2019-05, Vol.103, p.461-471

2019

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Identification of dynamic models in complex networks with prediction error methods—Basic methods for consistent module estimates

Automatica (Oxford), 2013-10, Vol.49 (10), p.2994-3006

2013

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On the accuracy in errors-in-variables identification compared to prediction-error identification

Automatica (Oxford), 2011-12, Vol.47 (12), p.2704-2712

2011

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System identification of nonlinear state-space models

Automatica (Oxford), 2011-01, Vol.47 (1), p.39-49

2011

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Identification of Hammerstein–Wiener models

Automatica (Oxford), 2013-01, Vol.49 (1), p.70-81

2013

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Identification of block-oriented nonlinear systems starting from linear approximations: A survey

Automatica (Oxford), 2017-11, Vol.85, p.272-292

2017

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Model-based reinforcement learning for approximate optimal regulation

Automatica (Oxford), 2016-02, Vol.64, p.94-104

2016

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Dealing with collinearity in large-scale linear system identification using Gaussian regression

Automatica (Oxford), 2024-08, Vol.166, p.111708, Article 111708

2024

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A flexible state–space model for learning nonlinear dynamical systems

Automatica (Oxford), 2017-06, Vol.80, p.189-199

2017

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On the smoothness of nonlinear system identification

Automatica (Oxford), 2020-11, Vol.121, p.109158, Article 109158

2020

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On the estimation of transfer functions, regularizations and Gaussian processes—Revisited

Automatica (Oxford), 2012-08, Vol.48 (8), p.1525-1535

2012

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Modeling and identification of uncertain-input systems

Bayesian frequentist bounds for machine learning and system identification

A Bayesian approach to sparse dynamic network identification

Errors-in-variables identification in dynamic networks — Consistency results for an instrumental variable approach

Subsystem identification of multivariable feedback and feedforward systems

Hammerstein system identification using nuclear norm minimization

Dual time–frequency domain system identification

Blind system identification using precise and quantized observations

Generic identifiability of subnetworks in a linear dynamic network: The full measurement case

Can errors-in-variables systems be identified from closed-loop experiments?

Optimal and two-step adaptive quantum detector tomography

Kernel methods in system identification, machine learning and function estimation: A survey

Convergence and consistency of recursive least squares with variable-rate forgetting

System identification in a networked environment using second order statistical properties

Robust MPC with recursive model update

Identification of dynamic models in complex networks with prediction error methods—Basic methods for consistent module estimates

On the accuracy in errors-in-variables identification compared to prediction-error identification

System identification of nonlinear state-space models

Identification of Hammerstein–Wiener models

Identification of block-oriented nonlinear systems starting from linear approximations: A survey

Model-based reinforcement learning for approximate optimal regulation

Dealing with collinearity in large-scale linear system identification using Gaussian regression

A flexible state–space model for learning nonlinear dynamical systems

On the smoothness of nonlinear system identification

On the estimation of transfer functions, regularizations and Gaussian processes—Revisited

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