  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.
