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Gradient-based adaptation of continuous dynamic model structures

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  • William G. La Cava
  • Kourosh Danai

Abstract

A gradient-based method of symbolic adaptation is introduced for a class of continuous dynamic models. The proposed model structure adaptation method starts with the first-principles model of the system and adapts its structure after adjusting its individual components in symbolic form. A key contribution of this work is its introduction of the model’s parameter sensitivity as the measure of symbolic changes to the model. This measure, which is essential to defining the structural sensitivity of the model, not only accommodates algebraic evaluation of candidate models in lieu of more computationally expensive simulation-based evaluation, but also makes possible the implementation of gradient-based optimisation in symbolic adaptation. The proposed method is applied to models of several virtual and real-world systems that demonstrate its potential utility.

Suggested Citation

  • William G. La Cava & Kourosh Danai, 2016. "Gradient-based adaptation of continuous dynamic model structures," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(1), pages 249-263, January.
    • Handle: RePEc:taf:tsysxx:v:47:y:2016:i:1:p:249-263
    DOI: 10.1080/00207721.2015.1069905
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