论文标题
通过标签指导的遗传编程发现动态系统关系的工具箱
Toolbox for Discovering Dynamic System Relations via TAG Guided Genetic Programming
论文作者
论文摘要
非线性动力学系统的数据驱动建模通常要求专家用户将关键决策作为标识过程的先验。最近，已经引入了一种基于\ textIt {genetic编程}（GP）和\ textit {aupcousation chanding grammars}（ago copotionaling语法}（TAG）的数据驱动建模的自动策略。当前的论文通过提出\ textit {多输入多输出}（MIMO）标签模型框架来扩展这些最新发现。此外，我们在MATLAB中引入了标签标识工具箱，该工具箱提供了提出的方法的实现，以解决NARMAX噪声假设下的多输入多输出识别问题。在识别两个SISO和一个MIMO非线性动力学基准模型中，该工具箱和建模方法的功能得到了证明。
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.
