Leithead, W.E. and Neo, K.S. and Leith, D.J. (2005) Gaussian regression based on models with two stochastic processes. In: 16th IFAC World Congress Conference, 2005-07-04 - 2005-07-08, Prague.
Full text not available in this repository. (Request a copy from the Strathclyde author)Abstract
When data contains components with different characteristics and it is required to identify both, standard Gaussian regression, based on a model with a single stochastic process, is inadequate. In this paper, a novel adaptation of Gaussian regression, based on models with two stochastic processes, is presented. In both the prior and posterior joint probability distributions, the Gaussian processes for the two components are independent. The effectiveness of the revised Gaussian regression method is demonstrated by application to wind turbine time series data.
| Item type: | Conference or Workshop Item (Paper) |
|---|---|
| ID code: | 37988 |
| Keywords: | gaussian regression, models, stochastic processes, identification, independent priors, gaussian processes, independent posteriors, 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: | 28 Feb 2012 16:19 |
| Last modified: | 04 Oct 2012 17:17 |
| URI: | http://strathprints.strath.ac.uk/id/eprint/37988 |
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