Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming

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@Article{NECHITA:2021:IFAC-PapersOnLinea,

• author = "Stefan-Cristian Nechita and Roland Toth and Dhruv Khandelwal and Maarten Schoukens",
• title = "Toolbox for Discovering Dynamic System Relations via {TAG} Guided Genetic Programming",
• journal = "IFAC-PapersOnLine",
• volume = "54",
• number = "7",
• pages = "379--384",
• year = "2021",
• note = "19th IFAC Symposium on System Identification SYSID 2021",
• ISSN = "2405-8963",
• URL = "https://research.tue.nl/en/publications/toolbox-for-discovering-dynamic-system-relations-via-tag-guided-g",
• DOI = "doi:10.1016/j.ifacol.2021.08.389",
• URL = "https://www.sciencedirect.com/science/article/pii/S2405896321011630",
• keywords = "genetic algorithms, genetic programming, Nonlinear system identification, Equation discovery, Tree Adjoining Grammar, Data-driven system modeling",
• 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",
• notes = "https://www.sysid2021.org/",

}

Genetic Programming entries for Stefan-Cristian Nechita Roland Toth Dhruv Khandelwal Maarten Schoukens

Citations