Derivative observations in Gaussian Process Models of Dynamic Systems

NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems

Volumn , Issue , 2002, Pages 1033-1040

  Solak, E ^a   Murray Smith, R ^b,c   Leithead, W E ^c   Leith, D J ^c   Rasmussen, C E ^d  

^a UNIVERSITY OF STRATHCLYDE   (United Kingdom)

^b UNIVERSITY OF GLASGOW   (United Kingdom)

^c NATIONAL UNIVERSITY OF IRELAND   (Ireland)

^d UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); NONLINEAR DYNAMICAL SYSTEMS;

DERIVATIVE INFORMATION; EMPIRICAL MODEL; GAUSSIAN PROCESS MODELS; GAUSSIAN PROCESSES; LEARNING PROCESS; MODELING OF DYNAMICS; NON-LINEAR DYNAMIC SYSTEMS; NON-PARAMETRIC MODEL; NORMAL FUNCTION; UNCERTAINTY;

COMPUTATIONAL EFFICIENCY;

EID: 85156217417     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. A. O'Hagan. On curve fitting and optimal design for regression (with discussion). Journal of the Royal Statistical Society B, 40:1-42, 1978.
2. C. K. I. Williams and C. E. Rasmussen. Gaussian processes for regression. In Neural Information Processing Systems - 8, pages 514-520, Cambridge, MA, 1996. MIT press.
3. C. K. I. Williams. Prediction with Gaussian processes: From linear regression to linear prediction and beyond. In M. I. Jordan, editor, Learning and Inference in Graphical Models, pages 599-621. Kluwer, 1998.
4. D. J. C. MacKay. Introduction to Gaussian Processes. NIPS'97 Tutorial notes., 1999.
5. A. O'Hagan. Some Bayesian numerical analysis. In J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, editors, Bayesian Statistics 4, pages 345-363. Oxford University Press, 1992.
6. C. E. Rasmussen. Gaussian processes to speed up Hybrid Monte Carlo for expensive Bayesian integrals. Draft: available at http://www.gatsby.ucl.ac.uk/edward/pub/gphmc.ps.gz, 2003.
7. R. Murray-Smith, T. A. Johansen, and R. Shorten. On transient dynamics, off-equilibrium behaviour and identification in blended multiple model structures. In European Control Conference, Karlsruhe, 1999, pages BA-14, 1999.
8. R. Murray-Smith and D. Sbarbaro. Nonlinear adaptive control using non-parametric Gaussian process prior models. In 15th IFAC World Congress on Automatic Control, Barcelona, 2002.
9. D. J. Leith, W. E. Leithead, E. Solak, and R. Murray-Smith. Divide & conquer identification: Using Gaussian process priors to combine derivative and non-derivative observations in a consistent manner. In Conference on Decision and Control, 2002.
10. R. Murray-Smith and T. A. Johansen. Multiple Model Approaches to Modelling and Control. Taylor and Francis, London, 1997.
11. T. Takagi and M. Sugeno. Fuzzy identification of systems and its applications for modeling and control. IEEE Trans. on Systems, Man and Cybernetics, 15(1):116-132, 1985.