Journal
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Volume 47, Issue 1, Pages 249-263Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2015.1069905
Keywords
evolutionary programming; dynamic modelling; system identification; symbolic regression; gradient-based adaptation
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Funding
- NSF [1068864]
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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.
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