论文标题
使用多基因遗传编程算法的非线性系统识别的Python库
A Python library for nonlinear system identification using Multi-Gene Genetic Programming algorithm
论文作者
论文摘要
模型可以直接从输入和输出数据槽构建，即一种称为系统标识的过程。具有外源输入（NARMAX）模型的非线性自回旋是该地区最常用的数学表示之一，并且在不同领域的数据驱动建模上有许多成功的应用。当这些模型具有高度的非线性和长期依赖性时，它们就会变得非常大。因此，必须执行结构选择过程以使其放大。在本文中，在Python中引入了一个工具箱，该工具箱使用名为Multi-Gene基因编程（MGGP）的进化算法执行结构选择过程。该工具箱封装了用于参数估计，仿真和验证的基本工具，并允许用户自定义其评估功能，包括个人结构中的先验知识和约束（灰色框标识）。
Models can be built directly from input and output data trough a process known as system identification. The Nonlinear AutoRegressive with eXogenous inputs (NARMAX) models are among the most used mathematical representations in the area and has many successful applications on data-driven modeling in different fields. Such models become extremely large when they have high degree of non-linearity and long-term dependencies. Hence, a structure selection process must be performed to make them parsimonious. In the present paper, it is introduced a toolbox in Python that performs the structure selection process using the evolutionary algorithm named Multi-Gene Genetic Programming (MGGP). The toolbox encapsulates basic tools for parameter estimation, simulation and validation, and it allows the users to customize their evaluation function including prior knowledge and constraints in the individual structure (gray-box identification).
