Data-driven modelling of dynamical systems using tree adjoining grammar and genetic programming

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Uittreksel

State-of-the-art methods for data-driven modelling of non-linear dynamical systems typically involve interactions with an expert user. In order to partially automate the process of modelling physical systems from data, many EA-based approaches have been proposed for model-structure selection, with special focus on non-linear systems. Recently, an approach for data-driven modelling of non-linear dynamical systems using Genetic Programming (GP) was proposed. The novelty of the method was the modelling of noise and the use of Tree Adjoining Grammar to shape the search-space explored by GP. In this paper, we report results achieved by the proposed method on three case studies. Each of the case studies considered here is based on real physical systems. The case studies pose a variety of challenges. In particular, these challenges range over varying amounts of prior knowledge of the true system, amount of data available, the complexity of the dynamics of the system, and the nature of non-linearities in the system. Based on the results achieved for the case studies, we critically analyse the performance of the proposed method.

TaalEngels
Titel2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's2673-2680
Aantal pagina's8
ISBN van elektronische versie978-1-7281-2153-6
DOI's
StatusGepubliceerd - 1 jun 2019
Evenement2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, Nieuw-Zeeland
Duur: 10 jun 201913 jun 2019

Congres

Congres2019 IEEE Congress on Evolutionary Computation, CEC 2019
LandNieuw-Zeeland
StadWellington
Periode10/06/1913/06/19

Vingerafdruk

Nonlinear dynamical systems
Genetic programming
Genetic Programming
Data-driven
Grammar
Data structures
Dynamical systems
Dynamical system
Computer systems programming
Model structures
Modeling
Nonlinear Dynamical Systems
Nonlinear systems
Physical Modeling
Prior Knowledge
Search Space
Nonlinear Systems
Nonlinearity
Interaction
Range of data

Trefwoorden

    Citeer dit

    Khandelwal, D., Schoukens, M., & Toth, R. (2019). Data-driven modelling of dynamical systems using tree adjoining grammar and genetic programming. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings (blz. 2673-2680). [8790250] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/CEC.2019.8790250
    Khandelwal, Dhruv ; Schoukens, Maarten ; Toth, Roland. / Data-driven modelling of dynamical systems using tree adjoining grammar and genetic programming. 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 2673-2680
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    Khandelwal, D, Schoukens, M & Toth, R 2019, Data-driven modelling of dynamical systems using tree adjoining grammar and genetic programming. in 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings., 8790250, Institute of Electrical and Electronics Engineers, Piscataway, blz. 2673-2680, Wellington, Nieuw-Zeeland, 10/06/19. DOI: 10.1109/CEC.2019.8790250

    Data-driven modelling of dynamical systems using tree adjoining grammar and genetic programming. / Khandelwal, Dhruv; Schoukens, Maarten; Toth, Roland.

    2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Piscataway : Institute of Electrical and Electronics Engineers, 2019. blz. 2673-2680 8790250.

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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    Khandelwal D, Schoukens M, Toth R. Data-driven modelling of dynamical systems using tree adjoining grammar and genetic programming. In 2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings. Piscataway: Institute of Electrical and Electronics Engineers. 2019. blz. 2673-2680. 8790250. Beschikbaar vanaf, DOI: 10.1109/CEC.2019.8790250