GAUSSIAN REGRESSION BASED ON MODELS WITH TWO STOCHASTIC PROCESSES
Author(s): W. E. Leithead, Kian Seng Neo, D. J. Leith
Type: Conference Paper (Proc. of the 16th IFAC)
Date: July 2005
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.
Conferences
- Leithead, W. E., Neo, Kian Seng and Leith, D. J., Gaussian regression based on models with two stochastic processes, 16th IFAC World Congress, Prague, July 2005.
- Leithead, W. E., Zhang, Yunong and Neo, Kian Seng, Wind turbine rotor acceleration: identification using Gaussian regression, 2nd ICINCO, Barcelona, September 2005.
- Neo, Kian Seng, Leithead, W. E., Zhang, Yunong, Multi-frequency scale Gaussian regression for noisy time-series data, 6th bi-ennial UKACC Control Conference, International Control Conference 2006, Glasgow, September 2006. (Accepted)
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