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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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Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of the marginal likelihood estimator

Automatica (Oxford), 2015-08, Vol.58, p.106-117

2015

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The quest for the right kernel in Bayesian impulse response identification: The use of OBFs

Automatica (Oxford), 2018-01, Vol.87, p.318-329

2018

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Linear Model Identification for Personalized Prediction and Control in Diabetes

IEEE transactions on biomedical engineering, 2022-02, Vol.69 (2), p.558-568

2022

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A New Kernel-Based Approach for NonlinearSystem Identification

IEEE transactions on automatic control, 2011-12, Vol.56 (12), p.2825-2840

2011

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Maximum Entropy vector kernels for MIMO system identification

Automatica (Oxford), 2017-05, Vol.79, p.326-339

2017

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A new kernel-based approach to hybrid system identification

Automatica (Oxford), 2016-08, Vol.70, p.21-31

Pillonetto, Gianluigi

2016

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System identification using kernel-based regularization: New insights on stability and consistency issues

Automatica (Oxford), 2018-07, Vol.93, p.321-332

Pillonetto, Gianluigi

2018

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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 refinement of the stability test for reproducing kernel Hilbert spaces

Automatica (Oxford), 2024-05, Vol.163, p.111606, Article 111606

2024

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Learning for Control: \mathcal -error Bounds for Kernel-based Regression

IEEE transactions on automatic control, 2024-03, p.1-16

2024

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Kernel-based linear system identification: When does the representer theorem hold?

Automatica (Oxford), 2024-01, Vol.159, p.111347, Article 111347

2024

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Mercer kernel absolute integrability is only sufficient for RKHS stability

Systems & control letters, 2024-09, Vol.191, p.105885, Article 105885

2024

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Sparse estimation in linear dynamic networks using the stable spline horseshoe prior

Automatica (Oxford), 2022-12, Vol.146, p.110666, Article 110666

2022

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A new kernel-based approach for linear system identification

Automatica (Oxford), 2010, Vol.46 (1), p.81-93

2010

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On the mathematical foundations of stable RKHSs

Automatica (Oxford), 2020-08, Vol.118, p.109038, Article 109038

2020

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In silico design and validation of a time-varying PID controller for an artificial pancreas with intraperitoneal insulin delivery and glucose sensing

APL bioengineering, 2023-06, Vol.7 (2), p.026105-026105

2023

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Sample Complexity and Minimax Properties of Exponentially Stable Regularized Estimators

IEEE transactions on automatic control, 2022-05, Vol.67 (5), p.2330-2342

2022

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Linear system identification using the sequential stabilizing spline algorithm

Automatica (Oxford), 2022-04, Vol.138, p.110169, Article 110169

2022

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On the stability of reproducing kernel Hilbert spaces of discrete-time impulse responses

Automatica (Oxford), 2018-09, Vol.95, p.529-533

2018

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Regularized System Identification: Learning Dynamic Models from Data

1st Edition 2022, 2022

Volltextzugriff (PDF)

Sliding-Mode Theory Under Feedback Constraints and the Problem of Epidemic Control

SIAM journal on applied mathematics, 2023-01, Vol.83 (6), p.2189-2211

2023

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COVID-19 epidemic control using short-term lockdowns for collective gain

Annual reviews in control, 2021, Vol.52, p.573-586

2021

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An inequality constrained nonlinear Kalman–Bucy smoother by interior point likelihood maximization

Automatica (Oxford), 2009, Vol.45 (1), p.25-33

2009

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The generalized cross validation filter

Automatica (Oxford), 2018-04, Vol.90, p.130-137

2018

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A Bayesian approach to sparse dynamic network identification

Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of the marginal likelihood estimator

The quest for the right kernel in Bayesian impulse response identification: The use of OBFs

Linear Model Identification for Personalized Prediction and Control in Diabetes

A New Kernel-Based Approach for NonlinearSystem Identification

Maximum Entropy vector kernels for MIMO system identification

A new kernel-based approach to hybrid system identification

System identification using kernel-based regularization: New insights on stability and consistency issues

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

A refinement of the stability test for reproducing kernel Hilbert spaces

Learning for Control: \mathcal -error Bounds for Kernel-based Regression

Kernel-based linear system identification: When does the representer theorem hold?

Mercer kernel absolute integrability is only sufficient for RKHS stability

Sparse estimation in linear dynamic networks using the stable spline horseshoe prior

A new kernel-based approach for linear system identification

On the mathematical foundations of stable RKHSs

In silico design and validation of a time-varying PID controller for an artificial pancreas with intraperitoneal insulin delivery and glucose sensing

Sample Complexity and Minimax Properties of Exponentially Stable Regularized Estimators

Linear system identification using the sequential stabilizing spline algorithm

On the stability of reproducing kernel Hilbert spaces of discrete-time impulse responses

Regularized System Identification: Learning Dynamic Models from Data

Sliding-Mode Theory Under Feedback Constraints and the Problem of Epidemic Control

COVID-19 epidemic control using short-term lockdowns for collective gain

An inequality constrained nonlinear Kalman–Bucy smoother by interior point likelihood maximization

The generalized cross validation filter

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