Bibliography on
Gaussian Process Models in Dynamic Systems Modelling
1999
R.
Murray-Smith, T. A. Johansen, R. Shorten.
In Proceedings of European Control Conference (ECC99), Paper BA-14, Karslruhe, 1999.
2000
D. J. Leith, R.
Murray-Smith, and W. E. Leithead.
Nonlinear structure identification:
A Gaussian process/velocity-based approach.
In Proceedings of the
UKACC Control Conference, Cambridge, 2000.
W. E. Leithead, D. J.
Leith, and R. Murray-Smith.
A Gaussian Process prior/Velocity-based Framework for
Nonlinear Modelling and Control.
In Irish Signals and
Systems Conference, Dublin, 2000.
2001
V. Babovic and M. Keijzer.
A
Gaussian process model applied to prediction of the water levels in Venice
lagoon.
In Proceedings of the
XXIX Congress of International Association for Hydraulic Research, 2001.
R. Murray-Smith and A.
Girard.
Gaussian process
priors with ARMA noise models.
In Proceedings of Irish
Signals and Systems Conference, Pages 147-152, Maynooth, 2001.
2002
B. Banko and J. Kocijan.
Uporaba Gaussovih procesov
za identifikacijo nelinearnih sistemov.
In B. Zajc, editor, Zbornik
enajste elektrotehniške in računalniške konference (ERK 2002), Volume A,
pages 323-326, Portorož, 2002. (in Slovene).
A. Girard, C. E.
Rasmussen, and R. Murray-Smith.
Gaussian
process priors with uncertain inputs: multiple-step-ahead prediction.
Technical Report DCS
TR-2002-119, University of Glasgow, Glasgow, 2002.
G. Gregorčič and G.
Lightbody.
Gaussian
processes for modelling of dynamic non-linear systems.
In Proceedings of Irish
Signals and Systems Conference, Cork, Pages 141-147, Cork, June 2002.
G. Gregorčič and G.
Lightbody.
Gaussian processes for
internal model control.
In A. Rakar, editor, Proceedings
of 3rd International PhD Workshop on Advances in Supervision and Control
Systems, A Young Generation Viewpoint, Pages 39-46, Strunjan, 2002.
J. Kocijan.
Gaussian process model based
predictive control.
Technical Report DP-8710,
Institut Jožef Stefan, Ljubljana, 2002.
J. Kocijan, B. Likar, B. Banko,
A. Girard, R. Murray-Smith, and C. E. Rasmussen.
Technical Report DP-8575,
Institut Jožef Stefan, Ljubljana, 2002.
D. Leith.
Determining nonlinear structure in time series data.
In Proceedings of
Workshop on Modern Methods for Data Intensive Modelling, Maynooth, 2002.
NUI Maynooth.
D. J. Leith, W. E.
Leithead, E. Solak, and R. Murray-Smith.
Divide and
conquer identification using Gaussian processes.
In Proceedings of the
41st Conference on Decision and Control, Pages 624-629, Las Vegas, AZ,
2002.
D. J. Leith, W. E.
Leithead, E. Solak, and R. Murray-Smith.
Divide and conquer identification using Gaussian
processes.
In C. Cowans, editor, Proceedings
of IEE Workshop on Nonlinear and Non-Gaussian signal processing (N2SP),
Peebles, UK, 2002.
R. Murray-Smith and D.
Sbarbaro.
Nonlinear
adaptive control using nonparametric Gaussian process prior models.
In Proceedings of IFAC
15th World Congress, Barcelona, 2002.
R. Murray-Smith, R.
Shorten, and D. Leith.
Nonparametric models of dynamic systems.
In C. Cowans, editor, Proceedings
of IEE Workshop on Nonlinear and Non-Gaussian signal processing (N2SP),
Peebles, UK, 2002.
2003
A. Girard, C. E.
Rasmussen, J. Quinonero-Candela, and R. Murray-Smith.
In S. Becker, S. Thrun,
and K. Obermayer, editors, Advances in Neural Information Processing Systems
conference, Volume 15, Pages 529-536. MIT Press, 2003.
G. Gray, R. Murray-Smith,
K. Thompson, and D. J. Murray-Smith.
Tutorial
example of Gaussian process prior modelling applied to twin-tank system.
Technical Report DCS
TR-2003-151, University of Glasgow, Glasgow, 2003.
G. Gregorčič and G.
Lightbody.
Internal
model control based on Gaussian process prior model.
In Proceedings of the
2003 American Control Conference (ACC 2003), pages 4981-4986, Denver, CO,
June 2003.
G. Gregorčič and G.
Lightbody.
From multiple model networks to the Gaussian processes
prior model.
In Proceedings of IFAC
ICONS conference, Pages 149-154, Faro, 2003.
G. Gregorčič and G.
Lightbody.
An
afine Gaussian process approach for nonlinear system identification.
Systems Science Journal, Volume 29, Issue 2, Pages 47-63, 2003.
J. Hansen.
Using Gaussian processes as a modelling tool in
control systems.
Technical Report DCS
TR-2003, University of Glasgow, Glasgow, 2003.
J. Kocijan, B. Banko, B. Likar,
A. Girard, R. Murray-Smith, and C. E. Rasmussen.
A case based comparison of identification with neural
networks and Gaussian process models.
In Proceedings of IFAC
ICONS conference, Volume 1, Pages 137-142, Faro, 2003.
J. Kocijan, A. Girard, B.
Banko, and R. Murray-Smith.
Dynamic systems identification with Gaussian
processes.
In I. Troch and F.
Breitenecker, editors, Proceedings of 4th IMACS Symposium on Mathematical
Modelling (MathMod), pages 776-784, Vienna, 2003.
J. Kocijan, A. Girard, and
D. J. Leith.
Incorporating linear
local models in Gaussian process model.
Technical Report DP-8895,
Institut Jožef Stefan, Ljubljana, December 2003.
J. Kocijan, R.
Murray-Smith, C. E. Rasmussen, and B. Likar.
Predictive
control with Gaussian process models.
In B. Zajc and M. Tkalčič,
editors, The IEEE Region 8 EUROCON 2003: computer as a tool, Volume A,
Pages 352-356, Ljubljana, 2003.
D. J. Leith and W. E.
Leithead.
Nonlinear structure identification with application to
Wiener-Hammerstein systems.
In Proceedings of 13th
IFAC Symposium on System Identification, Rotterdam, 2003.
W. E. Leithead, E. Solak,
and D. J. Leith.
Direct
identification of nonlinear structure using Gaussian process prior models.
In Proceedings of
European Control Conference (ECC 2003), Cambridge, 2003.
R. Murray-Smith, D.
Sbarbaro, C. E. Rasmussen, and A. Girard.
Adaptive,
cautious, predictive control with Gaussian process priors.
In Proceedings of 13th
IFAC Symposium on System Identification, Pages 1195-1200, Rotterdam, 2003.
J. Quinonero-Candela and
A. Girard.
Technical Report IMM-2003-18, Technical University Denmark, Informatics and Mathematical Modelling, Kongens Lyngby, 2003.
J. Quinonero-Candela, A.
Girard, J. Larsen, and C. E. Rasmussen.
In Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Volume 2, Pages 701-704, 2003.
D. Sbarbaro and R.
Murray-Smith.
Self-tuning
control of nonlinear systems using Gaussian process prior models.
Technical Report DCS
TR-2003-143, University of Glasgow, Glasgow, 2003.
E. Solak, R. Murray-Smith,
W. E. Leithead, D. J. Leith, and C. E. Rasmussen.
Derivative
observations in Gaussian process models of dynamic systems.
In S. Becker, S. Thrun,
and K. Obermayer, editors, Advances in Neural Information Processing Systems
conference, Volume 15, Pages 529-536. MIT Press, 2003.
2004
K. Ažman.
Identifikacija
dinamičnih sistemov z Gaussovimi procesi z vključenimi lokalnimi modeli.
Master's thesis, Univerza
v Ljubljani, Ljubljana, September 2004. (in Slovene).
A. Girard.
Approximate methods for
propagation of uncertainty with Gaussian process models.
PhD thesis, University of
Glasgow, Glasgow, 2004.
G. Gregorčič.
Data-based modelling of
nonlinear systems for control.
PhD thesis, University
College Cork, National University of Ireland, Cork, 2004.
J. Kocijan and D. J.
Leith.
Derivative
observations used in predictive control.
In Proceedings of
Melecon 2004, Volume 1, Pages 379-382, Dubrovnik, 12.-15. May 2004.
J. Kocijan, R.
Murray-Smith, C. E. Rasmussen, and A. Girard.
Gaussian
process model based predictive control.
In Proceedings of 4th
American Control Conference (ACC2004), Pages 2214-2218, Boston, MA, 30.
June-2. July 2004.
D. J. Leith, M. Heidl and
J. Ringwood.
Gaussian
process prior models for electrical load forecasting.
In 2004 International
Conference on Probabilistic Methods Applied to Power Systems, Pages
112-117. 2004.
B. Likar.
Prediktivno
vodenje nelinearnih sistemov na osnovi Gaussovih procesov.
Master's thesis, Univerza
v Ljubljani, Ljubljana, September 2004. (in Slovene).
C. E. Rasmussen and M. Kuss.
Gaussian
processes in reinforcement learning.
In S. Thrun, L. K. Saul,
and B. Schoelkopf, editors, Advances in Neural Information Processing
Systems conference, Volume 16, Pages 751-759. MIT Press, 2004.
D. Sbarbaro, R.
Murray-Smith, and A. Valdes.
In International
Symposium on Neural Networks. Springer Verlag, 2004.
2005
K. Ažman.
Incorporating prior knowledge
into Gaussian process model.
In Proceedings of 6th
International PhD Workshop on Systems and Control - A Young Generation
Viewpoint, Volume A, Pages 253-256,
Izola, 2005.
K. Ažman and J. Kocijan.
An example of Gaussian
process model identification.
In L. Budin and S.
Ribarić, editors, Proceedings of 28th International conference MIPRO, CIS -
Inteligent Systems, Pages 79-84, Opatija, maj 2005.
K. Ažman and J. Kocijan.
Identifikacija
dinamičnega sistema s histerezo z modelom na osnovi Gaussovih procesov.
In B. Zajc and A. Trost,
editors, Zbornik štirinajste elektrotehniške in računalniške konference (ERK
2005), Volume A, Pages 253-256, Portorož, 2005. (in Slovene).
K. Ažman and J. Kocijan.
Comprising
prior knowledge in dynamic Gaussian process models.
In Proceedings of the
International Conference on Computer Systems and Technologies (CompSysTech), Pages IIIB.2-1 – IIIB.2-6,
Varna, 2005.
Y. Engel, S. Mannor and R.
Meir.
Reinforcement
learning with Gaussian processes.
In Proceedings of the 22 nd International
Conference on Machine Learning,
Y. Engel, P. Szabo and D. Volkinshtein
Learning to
Control an Octopus Arm with Gaussian Process Temporal Difference Methods.
In Y. Weiss and B. Schoelkopf
and J. Platt , editors, Advances in Neural Information
Processing Systems, Volume 18, Pages 347-354. MIT Press, 2005.
B. Grašič.
Master's thesis, Univerza
v Ljubljani, Ljubljana, 2005. (in Slovene).
G. Gregorčič and G.
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Gaussian process approaches to nonlinear modelling and
control.
In
A. Ruano, editor, Intelligent control systems using computational
intelligence techniques,
IEE Intelligent Control
Series. IEE, 2005.
J. Hansen, R.
Murray-Smith, and T. A. Johansen.
In Joint 44th IEEE
conference on decision and control and European control conference (CDC-ECC
2005), Pages 7994-7999, Sevilla, 2005.
J. Kocijan and A. Girard.
Incorporating
linear local models in Gaussian process model.
In Proceedings of IFAC
16th World Congress, Praga, 2005.
J. Kocijan, A. Girard, B.
Banko, and R. Murray-Smith.
Dynamic
systems identification with Gaussian processes.
Mathematical and Computer
Modelling of Dynamic Systems, Volume 11,
Issue 4, Pages 411-424, December 2005.
J. Kocijan and R.
Murray-Smith.
Nonlinear
predictive control with Gaussian process model.
In Switching and
Learning in Feedback Systems, volume 3355 of Lecture Notes in Computer
Science, Pages 185-200. Springer, Heidelberg, 2005.
W. E. Leithead.
Identification
of nonlinear dynamic systems by combining equilibrium and off-equilibrium information.
In Proceedings of
International Conference on Industrial Electronics and Control Applications
(ICIECA), Quito, 2005.
W. E. Leithead, K. S. Neo,
and D. J. Leith.
Gaussian
regression based on models with two stochastic processes.
In Proceedings of IFAC
16th World Congress, Praga, 2005.
W. E. Leithead, Y. Zhang,
and D. J. Leith.
In Proceedings of IFAC
16th World Congress, Praga, 2005.
W. E. Leithead, Y. Zhang,
and D. J. Leith.
In Joint 44th IEEE
conference on decision and control and European control conference (CDC-ECC
2005), Sevilla, 2005.
W. E. Leithead, Y. Zhang,
and K.S. Neo.
Wind
turbine rotor acceleration: Identification using Gaussian regression.
In Proceedings of
International conference on informatics in control automation and robotics
(ICINCO), Barcelona, 2005.
R. Murray-Smith, B. A.
Pearlmutter.
Transformations
of Gaussian Process priors.
In
Deterministic and Statistical Methods in Machine Learning, Volume 3536 of Lecture
Notes in Artificial Intelligence, Pages
110-123. Springer, Heidelberg, 2005.
R. Palm.
Multi-step-ahead
prediction with Gaussian processes and TS-fuzzy models.
In Proceedings of 14th
IEEE International Conference on Fuzzy Systems, Pages 945-950, 2005.
D. Sbarbaro and R.
Murray-Smith.
Self-tuning
control of nonlinear systems using Gaussian process prior models.
In
Switching and Learning in Feedback Systems, Volume 3355 of Lecture Notes in Computer
Science, Pages 140-157. Springer, Heidelberg, 2005.
J. Q. Shi, R.
Murray-Smith, and D. M. Titterington.
Hierarchical
Gaussian process mixtures for regression.
Statistics and Computing, Volume 15, Pages 31-41,
2005.
J. M.Wang, D. J. Fleet,
and A. Hertzmann.
Gaussian
process dynamical models.
In Advances in Neural
Information Processing Systems, Volume 18, Pages 1441-1448. MIT Press,
2005.
Z.-H. Xiong, W.-Q. Zhang, Y.
Zhao, H.-H. Shao.
Thermal parameter soft sensor based on the mixture of
Gaussian processes.
Zhongguo Dianji Gongcheng Xuebao (Proc. Chin. Soc.
Electr. Eng.), Volume 25, Issue 7, Pages 30-33, 2005.
Z.-H. Xiong, H.-B. Yang, Y.-F.
Wu, H.-H. Shao.
Sparse GP-based soft sensor applied to the power
plant.
Zhongguo Dianji Gongcheng Xuebao (Proc. Chin. Soc.
Electr. Eng.), Volume 25, Issue 8, Pages 130-133,
2005.
Y. Zhang and W. E.
Leithead.
Applied Mathematics and
Computation, Volume 171, Issue 2, Pages 1264 -
1281, 2005.
2006
K. Ažman and J. Kocijan.
Gaussian process model validation: biotechnological
case studies.
In I. Troch and F.
Breitenecker, editors, Proceedings of the 5th Vienna Symposium on
Mathematical Modeling (MathMod), Vienna, 2006.
K. Ažman and J. Kocijan.
Identifikacija dinamičnega
sistema z znanim modelom šuma z modelom na osnovi Gaussovih procesov.
In B. Zajc and A. Trost,
editors, Zbornik petnajste elektrotehniške in računalniške konference (ERK
2006), Volume A, Pages 289-292, Portorož, 2006. (in Slovene).
K. Ažman and J. Kocijan.
An
application of Gaussian process models for control design.
In UKACC International
Control Conference, Glasgow, 2006.
P. Boyle.
Gaussian processes for
regression and optimisation.
PhD thesis, Victoria
University of Wellington, Wellington, New Zealand, 2006.
B. Grašič, P. Mlakar, and
M. Z. Božnar.
Ozone
prediction based on neural networks and Gaussian processes.
Nuovo Cimento della
Societa Italiana di Fisica, Sect. C, Volume 29, Issue 6, Pages 651-662, 2006.
Dj. Juričić and J.
Kocijan.
Fault detection based on Gaussian process model.
In I. Troch and F.
Breitenecker, editors, Proceedings of the 5th Vienna Symposium on
Mathematical Modeling (MathMod), Vienna, 2006.
M. Kuss.
Gaussian process models
for robust regression, classification and reinforcement learning.
PhD thesis, Technische
Universitaet Darmstadt, Darmstadt, 2006.
D. J. Leith, R. Murray-Smith,
and W. E. Leithead.
Inference of disjoint
linear and nonlinear subdomains of a nonlinear mapping.
Automatica, Volume 42, Issue 5,
Pages 849-858, May 2006.
K. Moon, V. Pavlović.
Impact
of dynamics on subspace embedding and tracking of sequences.
In Proceedings - 2006
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(CVPR 2006), Volume 1, Pages 198-205,
2006.
K. S. Neo, W. E. Leithead,
and Y. Zhang.
Multi
frequency scale Gaussian regression for noisy time-series data.
In UKACC International
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C. E. Rasmussen, C. K. I.
Williams.
Gaussian
processes for machine learning.
The MIT Press, Cambridge,
MA, London, 2006.
K. Thompson and D. J.
Murray-Smith.
Implementation of Gaussian process models for
nonlinear system identification.
In I. Troch and F.
Breitenecker, editors, Proceedings of the 5th Vienna Symposium on
Mathematical Modeling (MathMod), Vienna, 2006.
R. Urtasun, D. J. Fleet,
P. Fua.
3D
people tracking with Gaussian process dynamical models.
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Pattern Recognition (CVPR 2006), Volume 1, Pages
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2007
K. Ažman.
Identifikacija dinamičnih
sistemov z Gaussovimi procesi.
PhD thesis, Univerza v
Ljubljani, Ljubljana, 2007. (in Slovene).
K. Ažman and J. Kocijan.
Application of Gaussian
processes for black-box modelling of biosystems.
ISA Transactions, Volume 46, Issue 4, Pages
443-457, 2007.
S. Faul, G. Gregorčič, G.
Boylan, W. Marnane, S. Lightbody, G. Connolly.
Gaussian
process modelling of EEG for the detection of neonatal seizures.
IEEE Transactions on
Biomedical Engineering, Volume 54,
Issue 12, Pages: 2151 – 2162, 2007.
A. Grancharova, J. Kocijan
and T. A. Johansen.
Explicit
stohastic nonlinear predictive control based on Gaussian process models.
In Proceedings of the
EuropeanControl Conference (ECC 2007), Pages 2340-2347, Kos, 2007.
G. Gregorčič and G. Lightbody.
Local
model identification with Gaussian processes.
IEEE Transactions on
neural networks, Volume 18, Issue 5, Pages 1404-1423, 2007.
Hachino, T. Kadirkamanathan,
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Time
Series Forecasting Using Multiple Gaussian Process
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J. Ko, D. J. Klein, D. Fox, D. Haehnel.
Gaussian
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Autonomous Blimp.
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J. Ko, D. J. Klein, D. Fox, D. Haehnel.
GP-UKF:
Unscented Kalman Filters with Gaussian Process Prediction and Observation
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Identifikacija
nelinearnih sistemov z Gaussovimi procesi.
Modeliranje dinamičnih sistemov z umetnimi
nevronskimi mrežami in sorodnimi metodami.
Univerza v Novi Gorici, 2007, Pages 73-86. (in
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J. Kocijan and K. Ažman.
Gaussian Process Model Identification: A Process
Engineering Case Study.
In Proceedings of the
16th International Conference on Systems Science, Volume 1, Pages 418 -
427, Wroclaw, 2007.
J. Kocijan, K. Ažman and
A. Grancharova.
The Concept for
Gaussian Process Model Based System Identification Toolbox.
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Pages IIIA.23-1 - IIIA.23-6, Rousse, 2007.
J. Kocijan, B. Likar.
Gas-Liquid Separator Modelling and Simulation with
Gaussian Process Models.
In Proceedings of the
6th EUROSIM Congress on Modelling and Simulation ( EUROSIM 2007), 7 pages,
Ljubljana, 2007.
W. E. Leithead, Y. Zhang.
Communications In
Statistics-Simulation And Computation, Volume 36, Issue 2, Pages 367-380, 2007.
B. Likar and J. Kocijan.
Predictive control
of a gas-liquid separation plant based on a Gaussian process model.
Computers and Chemical
Engineering, Volume 31, Issue 3, Pages 142-152,
2007.
M. Neve, G. De Nicolao,
and L. Marchesi.
Nonparametric
identification of population models via Gaussian processes.
Automatica, Volume 43, Issue 7, Pages
1134-1144, 2007.
R. Palm.
Multiple-step-ahead
prediction in control systems with Gaussian process models and TS-fuzzy models.
Engineering Applications
of Artificial Intelligence, Volume 20, Issue 8, Pages 1023-1035, 2007.
J. M. Wang, D. J. Fleet,
and A. Hertzmann.
Multifactor
Gaussian Process models for style-content separation.
International Conference
on Machine Learning (ICML), Oregon, 2007.
Y. Zhang, W. E. Leithead.
Approximate
implementation of the logarithm of the matrix determinant in Gaussian process
regression.
Journal Of Statistical
Computation And Simulation, Volume 77, Issue 4, Pages 329-348, 2007.
2008
K. Ažman, J. Kocijan.
Non-linear
model predictive control for models with local information and uncertainties.
Trans.
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G. De Nicolao.
Subspace
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IEEE Conf. on Decision and Contro, CDC 2008, 2008.
J.
Choi, J. Lee and S. Oh.
Swarm
Intelligence for Achieving the Global Maximum using Spatio-Temporal Gaussian
Processes.
Proceedings of American Control Conference (ACC
2008),
J.
Choi, J. Lee and S. Oh.
Biologically-inspired
Navigation Strategies for Swarm Intelligence using Spatial Gaussian Processes.
Proceedings of IFAC 17th World Congress, Seoul, Korea, Pages
593-598, 2008
G.
De Nicolao, G. Pillonetto
A
new kernel-based approach for system identification.
Proceedings of American Control Conference (ACC
2008),
M.P.
Deisenroth, J. Peters, and C.E. Rasmussen.
Approximate
Dynamic Programming with Gaussian Processes.
Proceedings of American Control Conference (ACC
2008),
M.P. Deisenroth, C.E. Rasmussen, and J. Peters.
Model-Based Reinforcement Learning with Continuous States and
Actions.
Proceedings of the European Symposium on Artificial
Neural Networks (ESANN 2008), April 2008,
A. Grancharova, J. Kocijan
and T. A. Johansen.
Explicit stochastic predictive control of combustion plants based
on Gaussian process models.
Automatica, Volume 44, Issue 6, Pages
1621-1631, 2008.
G. Gregorčič and G.
Lightbody.
Nonlinear system
identification: From multiple-model networks to Gaussian processes.
Engineering Applications of Artificial Intelligence, Volume 21, Issue 7,
Pages 1035-1055, 2008.
T. Hachino and H. Takata.
Identification
of continuous-time nonlinear systems by using a Gaussian process model.
IEEJ Transactions on Electrical and Electronic
Engineering, Volume 3 Issue 6,
Pages 620 – 628, 2008.
J. Ko and D. Fox.
GP-BayesFilters:
Bayesian Filtering Using Gaussian Process Prediction and Observation Models.
Proceedings of the 2008
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). September 2008, Nice, France, Pages 3471 - 3476.
J. Kocijan.
Gaussian process models for systems
identification.
Proceedings of the 9th International PhD Workshop on Systems and Contro: young generation viewpoint. Izola, Simonov zaliv, 2008, 8 pages.
J. Kocijan, K. Ažman.
Gaussian
process model identification : a process engineering case study.
Systems Science Journal, Volume 34, Issue 3, Pages 31-38, 2008.
J. Kocijan, B. Likar.
Gas–liquid separator
modelling and simulation with Gaussian-process models.
Simulation Modelling Practice and Theory, Volume 16, Issue 8,
Pages 910-922, 2008
K.S. Neo.
Nonlinear
Dynamics Identification Using Gaussian Process Prior Models Within a Bayesian
Context.
PhD thesis, National
University of Ireland, Maynooth, 2008.
D. Nguyen-Tuong, J. Peters.
Learning
Robot Dynamics for Computed Torque Control Using Local Gaussian Processes
Regression.
Symposium on
Learning and Adaptive Behaviors for Robotic Systems, 2008. LAB-RS '08.
ECSIS, Pages 59 – 64.
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M. Seeger, B. Schoelkopf.
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Inverse Dynamics: a Comparison.
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D. Nguyen-Tuong, M.
Seeger, J. Peters.
Computed
torque control with nonparametric regression models.
Proceedings of the 2008 American Control Conference, ACC 2008, 2008.
G. Pillonetto, A. Chiuso,
G. De Nicolao.
Predictor
estimation via Gaussian regression.
Proceedings of the 2008
IEEE Conf. on Decision and Contro, CDC 2008, 2008.
L. Pronzato.
Optimal experimental
design and some related control problems.
Automatica, Volume 44, Issue 2, Pages 303-325,
2008.
C.E. Rasmussen
and M.P. Deisenroth.
Probabilistic
Inference for Fast Learning in Control.
chapter in Recent Advances in Reinforcement
Learning, Lecture Notes on Computer Science, LNAI series, Volume 5323,
Springer-Verlag, November 2008, Pages. 229–242.
F. di Sciascio,
A.N. Amicarelli.
Biomass Estimation
in Batch Biotechnological Processes by Bayesian Gaussian Process Regression.
Computers and Chemical
Engineering, Volume 32, Issue 12, Pages
3264-3273, 2008.
S. Vinet and E. Vazquez
Black-box
identification and simulation of continuous-time nonlinear systems with random
processes.
Proceedings of
the IFAC 17th World Congress, Seoul, Korea, Pages 14391-14396, 2008
J. M. Wang, D. J. Fleet, and
A. Hertzmann.
Gaussian Process Dynamical Models for Human Motion.
IEEE
Transactions on Pattern Analysis and Machine Intelligence,Volume 30, Issue 2, Pages 283 – 298,
2008.
J. M. Wang, D. J. Fleet,
and A. Hertzmann.
IEEE Transactions on
Pattern Analysis and Machine Intelligence,Volume 30, Issue 6, Page 1118, 2008.
J.
Yuan, K. Wang, T. Yu, M. Fang.
International Journal of
Machine Tools and Manufacture,Volume 48, Issue 1, Pages
47-60, 2008.
2009
K. Ažman, J. Kocijan
Fixed-structure
Gaussian process model.
International Journal of
Systems Science. Volume 40, Issue 12, Pages 1253–1262.
J.
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E-mail: jus[dot]kocijan[at]ijs[dot]si