Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming

TL;DR

This paper introduces a MATLAB toolbox and methodology for automated data-driven modeling of nonlinear dynamical systems, extending previous SISO approaches to MIMO systems using TAG-guided genetic programming.

Contribution

It extends TAG-guided genetic programming to multi-input multi-output systems and provides a practical MATLAB toolbox for NARMAX model identification.

Findings

Successfully identified benchmark nonlinear models

Demonstrated effectiveness on SISO and MIMO systems

Provides a new tool for automated system modeling

Abstract

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.