Toolbox for discovering dynamic system relations via TAG guided genetic programming
| Autor: | Stefan Cristian Nechita, Maarten Schoukens, Dhruv Khandelwal, Roland Tóth |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Nonlinear system identification
Computer science Genetic Programming QA75 Electronic computers. Computer science / számítástechnika számítógéptudomány Genetic programming Systems and Control (eess.SY) computer.software_genre Electrical Engineering and Systems Science - Systems and Control Toolbox Data-driven system modeling Tree Adjoining Grammar Data-driven Identification (information) Tree (data structure) Control and Systems Engineering Benchmark (computing) FOS: Electrical engineering electronic engineering information engineering Equation discovery Data mining MATLAB computer computer.programming_language |
| Zdroj: | IFAC-PapersOnLine. 54(7):379-384 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2021.08.389 |
| Popis: | Data-driven modeling of nonlinear dynamical systems often require an expert user to take critical decisions a priori to the identification procedure. Recently an automated strategy for data driven modeling of \textit{single-input single-output} (SISO) nonlinear dynamical systems based on \textit{Genetic Programming} (GP) and \textit{Tree Adjoining Grammars} (TAG) has been introduced. The current paper extends these latest findings by proposing a \textit{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 modelling methodology are demonstrated in the identification of two SISO and one MIMO nonlinear dynamical benchmark models. |
| Databáze: | OpenAIRE |
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