Toolbox for discovering dynamic system relations via TAG guided genetic programming

Stefan Cristian Nechita; Roland Tóth; Dhruv Khandelwal; Maarten Schoukens

doi:10.1016/j.ifacol.2021.08.389

Toolbox for discovering dynamic system relations via TAG guided genetic programming

Stefan Cristian Nechita, Roland Tóth, Dhruv Khandelwal, Maarten Schoukens

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

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Abstract

Data-driven modeling of nonlinear dynamical systems often requires an expert user to take critical decisions a priori to the identification procedure. Recently, an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on genetic programming (GP) and tree adjoining grammars (TAG) was introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover, we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modeling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.

Original language	English
Pages (from-to)	379-384
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	54
Issue number	7
DOIs	https://doi.org/10.1016/j.ifacol.2021.08.389
Publication status	Published - 1 Jul 2021
Event	19th IFAC Symposium on System Identification (SYSID 2021) - Virtual, Padova, Italy Duration: 13 Jul 2021 → 16 Jul 2021 Conference number: 19 https://www.sysid2021.org/

Bibliographical note

Funding Information:
This research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program and by the Dutch Organization for Scientific Research (NWO, domain TTW, grant: 13852) which is partly funded by the Ministry of Economic Affairs of The Netherlands.

Keywords

Data-driven system modeling
Equation discovery
Genetic Programming
Nonlinear system identification
Tree Adjoining Grammar

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title = "Toolbox for discovering dynamic system relations via TAG guided genetic programming",

abstract = "Data-driven modeling of nonlinear dynamical systems often requires an expert user to take critical decisions a priori to the identification procedure. Recently, an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on genetic programming (GP) and tree adjoining grammars (TAG) was introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover, we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modeling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.",

keywords = "Data-driven system modeling, Equation discovery, Genetic Programming, Nonlinear system identification, Tree Adjoining Grammar",

author = "Nechita, {Stefan Cristian} and Roland T{\'o}th and Dhruv Khandelwal and Maarten Schoukens",

note = "Funding Information: This research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program and by the Dutch Organization for Scientific Research (NWO, domain TTW, grant: 13852) which is partly funded by the Ministry of Economic Affairs of The Netherlands. ; 19th IFAC Symposium on System Identification (SYSID 2021), SYSID 2021 ; Conference date: 13-07-2021 Through 16-07-2021",

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T1 - Toolbox for discovering dynamic system relations via TAG guided genetic programming

AU - Nechita, Stefan Cristian

AU - Tóth, Roland

AU - Khandelwal, Dhruv

AU - Schoukens, Maarten

N1 - Conference code: 19

PY - 2021/7/1

Y1 - 2021/7/1

N2 - Data-driven modeling of nonlinear dynamical systems often requires an expert user to take critical decisions a priori to the identification procedure. Recently, an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on genetic programming (GP) and tree adjoining grammars (TAG) was introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover, we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modeling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.

AB - Data-driven modeling of nonlinear dynamical systems often requires an expert user to take critical decisions a priori to the identification procedure. Recently, an automated strategy for data driven modeling of single-input single-output (SISO) nonlinear dynamical systems based on genetic programming (GP) and tree adjoining grammars (TAG) was introduced. The current paper extends these latest findings by proposing a multi-input multi-output (MIMO) TAG modeling framework for polynomial NARMAX models. Moreover, we introduce a TAG identification toolbox in Matlab that provides implementation of the proposed methodology to solve multi-input multi-output identification problems under NARMAX noise assumption. The capabilities of the toolbox and the modeling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models.

KW - Data-driven system modeling

KW - Equation discovery

KW - Genetic Programming

KW - Nonlinear system identification

KW - Tree Adjoining Grammar

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DO - 10.1016/j.ifacol.2021.08.389

M3 - Conference article

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JO - IFAC-PapersOnLine

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T2 - 19th IFAC Symposium on System Identification (SYSID 2021)

Y2 - 13 July 2021 through 16 July 2021

ER -

Toolbox for discovering dynamic system relations via TAG guided genetic programming

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Keywords

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