# Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming

arXiv: https://arxiv.org/abs/2012.08834 · HTML: https://mlsanity.com/paper/2012.08834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2020.

## Built on by (most cited first, 1 of 1)

- [Sparse Bayesian Deep Learning for Dynamic System Identification](https://mlsanity.com/paper/2107.12910/) (2021)

## Abstract (arXiv)

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 textitsingle-input single-output (SISO) nonlinear dynamical systems based on textitGenetic Programming (GP) and textitTree Adjoining Grammars (TAG) has been introduced. The current paper extends these latest findings by proposing a textitmulti-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.