论文标题
通过连续图近似的非线性测量的迭代数据驱动推断
Iterative data-driven inference of nonlinearity measures via successive graph approximation
论文作者
论文摘要
在本文中，我们建立了一种迭代数据驱动的方法，以在未知非线性系统的非线性度量上得出保证的界限。在这种情况下，非线性测量通过其输入输出行为与一组线性模型的距离来量化动力系统非线性的强度。首先，我们根据基于数据的非参数设置会员形式表示地面真相系统和非线性度量的局部推断，通过给定输入样本来计算这些度量的保证上限。其次，我们提出了一种算法，通过未知输入输出行为的进一步样本在迭代中改善这种界限。
In this paper, we establish an iterative data-driven approach to derive guaranteed bounds on nonlinearity measures of unknown nonlinear systems. In this context, nonlinearity measures quantify the strength of the nonlinearity of a dynamical system by the distance of its input-output behaviour to a set of linear models. First, we compute a guaranteed upper bound of these measures by given input-output samples based on a data-based non-parametric set-membership representation of the ground-truth system and local inferences of nonlinearity measures. Second, we propose an algorithm to improve this bound iteratively by further samples of the unknown input-output behaviour.
