论文标题
随机动力系统模型的毗邻语法表示
A Tree Adjoining Grammar Representation for Models Of Stochastic Dynamical Systems
论文作者
论文摘要
模型结构和复杂性选择在系统识别中仍然是一个具有挑战性的问题，尤其是对于参数非线性模型。文献中已经提出了许多基于进化算法的方法（EA）方法，用于估计模型结构和复杂性。在大多数情况下，设计了提出的方法是为了估算指定模型类内的结构和复杂性，因此这些方法不会扩展到没有重大变化的其他模型结构。在本文中，我们为随机参数模型提出了一个毗邻语法（TAG）的树。标签可用于在EA框架中生成模型，同时施加理想的结构约束并纳入先验知识。在本文中，我们提出了一个可以系统地生成从FIR到多项式Narmax模型的模型的标签。此外，我们证明标签可以很容易地扩展到更通用的模型类，例如非线性框 - 詹金斯模型类，从而实现了通过EA的灵活和自动模型结构以及复杂性选择的实现。
Model structure and complexity selection remains a challenging problem in system identification, especially for parametric non-linear models. Many Evolutionary Algorithm (EA) based methods have been proposed in the literature for estimating model structure and complexity. In most cases, the proposed methods are devised for estimating structure and complexity within a specified model class and hence these methods do not extend to other model structures without significant changes. In this paper, we propose a Tree Adjoining Grammar (TAG) for stochastic parametric models. TAGs can be used to generate models in an EA framework while imposing desirable structural constraints and incorporating prior knowledge. In this paper, we propose a TAG that can systematically generate models ranging from FIRs to polynomial NARMAX models. Furthermore, we demonstrate that TAGs can be easily extended to more general model classes, such as the non-linear Box-Jenkins model class, enabling the realization of flexible and automatic model structure and complexity selection via EA.
