On ASMOD — An Algorithm for Empirical Modelling using Spline Functions

  • Tom Kavli
  • Erik Weyer
Part of the Advances in Industrial Control book series (AIC)


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.


Gluconic Acid Multivariate Adaptive Regression Spline Minimum Description Length Structural Risk Minimisation Spline Space 
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 London Limited 1995

Authors and Affiliations

  • Tom Kavli
    • 1
  • Erik Weyer
    • 2
  1. 1.SINTEFOsloNorway
  2. 2.Department of Chemical EngineeringThe University of QueenslandBrisbaneAustralia

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