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.
|Number of pages||6|
|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
Bibliographical noteFunding 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.
- Data-driven system modeling
- Equation discovery
- Genetic Programming
- Nonlinear system identification
- Tree Adjoining Grammar