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Building Structure-Property Predictive Models Using Data Assimilation

  • Hamse Y. Mussa
  • David J. Lary
  • Robert C. Glen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4216)

Abstract

In Chemometrics it is often the norm to develop regression methods for analysing non-linear multivariate data by using the observations (measurements) as the sole constraint. This is the case regardless of the nature of the regression method (parametric or non-parametric)[1]. In this article we present the development of a regression model using data assimilation[2] – A technique that takes into account additional available information about the “system” which the model is to represent. The new approach shows substantial improvement over the “conventional” methods[3] against which it has been compared.

Keywords

Data Assimilation Skill Forecast Innovation Vector Neural Network Algorithm Data Assimilation Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hamse Y. Mussa
    • 1
  • David J. Lary
    • 2
  • Robert C. Glen
    • 1
  1. 1.Unilever Centre for Molecular Sciences Informatics, Department of ChemistryUniversity of CambridgeCambridgeU.K
  2. 2.NASAGoddard Space Flight CentreGreenbeltUSA

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