Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{NECHITA:2021:IFAC-PapersOnLinea,
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author = "Stefan-Cristian Nechita and Roland Toth and
Dhruv Khandelwal and Maarten Schoukens",
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title = "Toolbox for Discovering Dynamic System Relations via
{TAG} Guided Genetic Programming",
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journal = "IFAC-PapersOnLine",
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volume = "54",
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number = "7",
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pages = "379--384",
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year = "2021",
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note = "19th IFAC Symposium on System Identification SYSID
2021",
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ISSN = "2405-8963",
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URL = "https://research.tue.nl/en/publications/toolbox-for-discovering-dynamic-system-relations-via-tag-guided-g",
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DOI = "doi:10.1016/j.ifacol.2021.08.389",
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URL = "https://www.sciencedirect.com/science/article/pii/S2405896321011630",
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keywords = "genetic algorithms, genetic programming, Nonlinear
system identification, Equation discovery, Tree
Adjoining Grammar, Data-driven system modeling",
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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",
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notes = "https://www.sysid2021.org/",
- }
Genetic Programming entries for
Stefan-Cristian Nechita
Roland Toth
Dhruv Khandelwal
Maarten Schoukens
Citations