ABSTRACT

In many engineering applications, continuous-time models are preferred to discrete-time ones, in that they provide good physical insight and can be derived also from non-uniformly sampled data. However, for such models, model selection is a hard task if no prior physical knowledge is given. In this paper, we propose a non-parametric approach to infer a continuous-time linear model from data, by automatically selecting a proper structure of the transfer function and guaranteeing to preserve the system stability properties. By means of benchmark simulation examples, the proposed approach is shown to outperform state-of-the-art continuous-time methods, also in the critical case when short sequences of canonical input signals, like impulses or steps, are used for model learning.

摘要

在许多工程应用中，连续时间模型优于离散时间模型，因为它们提供了良好的物理洞察力，并且还可以从非均匀采样的数据中导出。然而，对于这样的模型，如果没有先验物理知识，模型选择是一项艰巨的任务。在本文中，我们提出了一种非参数方法，通过自动选择传递函数的适当结构并保证保持系统稳定性特性，从数据中推断出连续时间线性模型。通过基准仿真示例，所提出的方法被证明优于最先进的连续时间方法，在关键情况下，当标准输入信号的短序列（如脉冲或步骤）用于模型学习时也是如此。