Print Email Facebook Twitter Constructing parsimonious analytic models for dynamic systems via symbolic regression Title Constructing parsimonious analytic models for dynamic systems via symbolic regression Author Derner, Erik (Czech Technical University) Kubalík, Jiří (Czech Technical University) Ancona, N. (TU Delft Learning & Autonomous Control) Babuska, R. (TU Delft Learning & Autonomous Control; Czech Technical University) Date 2020 Abstract Developing mathematical models of dynamic systems is central to many disciplines of engineering and science. Models facilitate simulations, analysis of the system's behavior, decision making and design of automatic control algorithms. Even inherently model-free control techniques such as reinforcement learning (RL) have been shown to benefit from the use of models, typically learned online. Any model construction method must address the tradeoff between the accuracy of the model and its complexity, which is difficult to strike. In this paper, we propose to employ symbolic regression (SR) to construct parsimonious process models described by analytic equations. We have equipped our method with two different state-of-the-art SR algorithms which automatically search for equations that fit the measured data: Single Node Genetic Programming (SNGP) and Multi-Gene Genetic Programming (MGGP). In addition to the standard problem formulation in the state-space domain, we show how the method can also be applied to input–output models of the NARX (nonlinear autoregressive with exogenous input) type. We present the approach on three simulated examples with up to 14-dimensional state space: an inverted pendulum, a mobile robot, and a bipedal walking robot. A comparison with deep neural networks and local linear regression shows that SR in most cases outperforms these commonly used alternative methods. We demonstrate on a real pendulum system that the analytic model found enables a RL controller to successfully perform the swing-up task, based on a model constructed from only 100 data samples. Subject Genetic programmingModel learningReinforcement learningSymbolic regression To reference this document use: http://resolver.tudelft.nl/uuid:365bf499-8102-4a6d-8045-574530735d55 DOI https://doi.org/10.1016/j.asoc.2020.106432 Embargo date 2022-06-10 ISSN 1568-4946 Source Applied Soft Computing, 94 Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type journal article Rights © 2020 Erik Derner, Jiří Kubalík, N. Ancona, R. Babuska Files PDF derner2020constructing_footer.pdf 892.92 KB Close viewer /islandora/object/uuid:365bf499-8102-4a6d-8045-574530735d55/datastream/OBJ/view