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Recursive lazy learning for modeling and control

  • Gianluca Bontempi
  • Mauro Birattari
  • Hugues Bersini
Instance Based Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)

Abstract

This paper presents a local method for modeling and control of non-linear dynamical systems from input-output data. The proposed methodology couples a local model identification inspired by the lazy learning technique, with a control strategy based on linear optimal control theory. The local modeling procedure uses a query-based approach to select the best model configuration by assessing and comparing different alternatives. A new recursive technique for local model identification and validation is presented, together with an enhanced statistical method for model selection. The control method combines the linearization provided by the local learning techniques with optimal linear control theory, to control non-linear systems in configurations which are far from equilibrium. Simulations of the identification of a non-linear benchmark model and of the control of a complex non-linear system (the bioreactor) are presented. The experimental results show that the approach can obtain better performance than neural networks in identification and control, even using smaller training data sets.

Keywords

Near Neighbor Query Point Optimal Control Theory Linearize Quadratic Regulator Local Learning 
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 1998

Authors and Affiliations

  • Gianluca Bontempi
    • 1
  • Mauro Birattari
    • 1
  • Hugues Bersini
    • 1
  1. 1.Iridia - CP 194/6 Université Libre de Bruxelles 50BruxellesBelgium

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