Constructing parsimonious analytic models for dynamic systems via symbolic regression

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00341607" target="_blank" >RIV/68407700:21230/20:00341607 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/20:00341607

Result on the web

<a href="https://doi.org/10.1016/j.asoc.2020.106432" target="_blank" >https://doi.org/10.1016/j.asoc.2020.106432</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2020.106432" target="_blank" >10.1016/j.asoc.2020.106432</a>

Result language

angličtina

Original language name

Constructing parsimonious analytic models for dynamic systems via symbolic regression

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF15_003%2F0000470" target="_blank" >EF15_003/0000470: Robotics 4 Industry 4.0</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Soft Computing

ISSN

1568-4946

e-ISSN

1872-9681

Volume of the periodical

94

Issue of the periodical within the volume

September

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

12

Pages from-to

—

UT code for WoS article

000566907500010

EID of the result in the Scopus database

2-s2.0-85086143418