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On ASMOD — An Algorithm for Empirical Modelling using Spline Functions

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Neural Network Engineering in Dynamic Control Systems

Part of the book series: Advances in Industrial Control ((AIC))

Abstract

Empirical modelling algorithms build mathematical models of systems based on observed data. This chapter describes the theoretical foundation and principles of the ASMOD algorithm, including some improvements on the original algorithm. The ASMOD algorithm uses B-splines for representing general nonlinear models of several variables. The internal structure of the model is, through an incremental refinement procedure, automatically adapted to the dependencies observed in the data. Only input variables which are found of relevance are included in the model, and the dependency of different variables are decoupled when possible. This makes the model more parsimonious and also more transparent to the user. Case studies are included which confirm the usefulness of the algorithm.

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Author information

Authors and Affiliations

  1. SINTEF, Box 124, Blindern, 0314, Oslo, Norway

    Tom Kavli

  2. Department of Chemical Engineering, The University of Queensland, Brisbane, QLD, 4072, Australia

    Erik Weyer

Authors
  1. Tom Kavli
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  2. Erik Weyer
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Editor information

Editors and Affiliations

  1. Daimler-Benz AG, Alt Moabit 96 a, D-10559, Berlin, Germany

    Kenneth J. Hunt

  2. Department of Electrical and Electronic Engineering, Queen’s University of Belfast, Belfast, BT9 5AH, UK

    George R. Irwin

  3. Department of Cybernetics, School of Engineering and Information Sciences, University of Reading, P.O. Box 225, Whiteknights, Reading, RG6 2AY, UK

    Kevin Warwick

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© 1995 Springer-Verlag London Limited

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Kavli, T., Weyer, E. (1995). On ASMOD — An Algorithm for Empirical Modelling using Spline Functions. In: Hunt, K.J., Irwin, G.R., Warwick, K. (eds) Neural Network Engineering in Dynamic Control Systems. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-3066-6_5

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  • DOI: https://doi.org/10.1007/978-1-4471-3066-6_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-3068-0

  • Online ISBN: 978-1-4471-3066-6

  • eBook Packages: Springer Book Archive

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