Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming

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 $\mathit{\text{Tree}}\mathit{\text{Adjoining}}\mathit{\text{Grammars}}$ (TAG) has been introduced. The current paper extends these latest findings by proposing a $\mathit{\text{multi-input}}\mathit{\text{multi-output}}$ (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.