Pepijn B. Cox
Title
Cited by
Cited by
Year
LPV system identification under noise corrupted scheduling and output signal observations
D Piga, P Cox, R Toth, V Laurain
Automatica 53, 329-338, 2015
322015
Bayesian identification of LPV Box-Jenkins models
M Darwish, P Cox, G Pillonetto, R Tóth
Decision and Control (CDC), 2015 IEEE 54th Annual Conference on, 66-71, 2015
102015
Towards efficient maximum likelihood estimation of LPV-SS models
PB Cox, R Tóth, M Petreczky
Automatica 97, 392-403, 2018
82018
Estimation of LPV-SS models with static dependency using correlation analysis
PB Cox, R Tóth, M Petreczky
IFAC-PapersOnLine 48 (26), 91-96, 2015
82015
Affine parameter-dependent lyapunov functions for LPV systems with affine dependence
PB Cox, S Weiland, R Tóth
IEEE Transactions on Automatic Control, 2019
72019
Towards efficient identification of linear parameter-varying state-space models
PB Cox
Eindhoven University of Technology, 2018
72018
Predictionerror identification of LPV systems: a nonparametric gaussian regression approach
R Darwish, M. A. H. and Cox, P. B. and Proimadis, I. and Pillonetto, G. and Tóth
Automatica 97, 92-103, 2018
52018
Alternative form of predictor based identification of LPV-SS models with innovation noise
P Cox, R Tóth
Decision and Control (CDC), 2016 IEEE 55th Conference on, 1223-1228, 2016
52016
LPV State-space model identification in the Bayesian setting: A 3-step procedure
PB Cox, R Tóth
American Control Conference (ACC), 2016, 4604-4610, 2016
42016
CVA identification of nonlinear systems with LPV state-space models of affine dependence
WE Larimore, PB Cox, R Tóth
American Control Conference (ACC), 2015, 831-837, 2015
32015
LPV state-space identification via IO methods and efficient model order reduction in comparison with subspace methods
E Schulz, PB Cox, R Toth, H Werner
Decision and Control (CDC), 2017 IEEE 56th Annual Conference on, 3575-3581, 2017
22017
System for deterring birds
SFB Henskes, PRC Tammes, T Sprang, PB Cox
US Patent App. 15/105,854, 2017
22017
Description of the data generating system utilized in “prediction-error identification of LPV systems: a nonparametric gaussian regression approach”
MAH Darwish, PB Cox, I Proimadis, G Pillonetto, R Toth
Eindhoven University of Technology, Tech. Rep. TUE-CS-2017-001, 2017
22017
On the connection between different noise structures for LPV-SS models
PB Cox, R Tóth
arXiv preprint arXiv:1610.09173, 2016
12016
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Articles 1–14