Gaussian regression based on models with two stochastic processes

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. (https://doi.org/10.3182/20050703-6-CZ-1902.00024)

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.

• Share and Export
  

  RDF+XMLBibTeXRIOXX2 XMLRDF+N-TriplesJSONDublin CoreAtomSimple MetadataReferMETSHTML CitationASCII CitationOpenURL ContextObjectEndNoteOpenURL ContextObject in SpanMODSMPEG-21 DIDLEP3 XMLData Cite XMLRIS (EndNote Online, Zotero, Ref manager, etc)RDF+N3Multiline CSV
• Item metadata

  Item type:	Conference or Workshop Item(Paper)
  ID code:	37988
  Dates:	Date Event 2005 Published
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering
  Depositing user:	Pure Administrator
  Date deposited:	28 Feb 2012 16:19
  Last modified:	09 Apr 2024 05:06
  URI:	https://strathprints.strath.ac.uk/id/eprint/37988