Solak, E. and Murray-Smith, R. and Leithead, W.E. and Rasmusson, C. and Leith, D.J. (2002) Derivative observations in Gaussian process models of dynamic systems. In: 2002 Neural Information Processing (NIPS) Meeting, 2002-12-09 - 2002-12-11, Vancouver.
Full text not available in this repository. (Request a copy from the Strathclyde author)Abstract
Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1) It allows us to combine derivative information, and associated uncertainty with normal function observations into the learning and inference process.
| Item type: | Conference or Workshop Item (Paper) |
|---|---|
| ID code: | 38601 |
| Keywords: | derivative observations, gaussian process models, dynamic systems, Electrical engineering. Electronics Nuclear engineering |
| Subjects: | Technology > Electrical engineering. Electronics Nuclear engineering |
| Department: | Faculty of Engineering > Electronic and Electrical Engineering |
| Related URLs: | |
| Depositing user: | Pure Administrator |
| Date Deposited: | 20 Mar 2012 15:08 |
| Last modified: | 04 Oct 2012 17:11 |
| URI: | http://strathprints.strath.ac.uk/id/eprint/38601 |
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