基于逐次图逼近的非线性测度的迭代数据驱动推理
Iterative data-driven inference of nonlinearity measures via successive graph approximation
摘要：
本文建立了一种迭代的数据驱动方法来求解未知非线性系统的非线性测度的保界。在这种情况下，非线性度量通过其输入-输出行为与一组线性模型的距离来量化动力系统的非线性强度。首先，我们通过给定的输入-输出样本，基于数据的非参数集员表示的地面实况系统和非线性措施的局部推断，计算这些措施的保证上界。其次，我们提出了一个算法，以提高这个界限迭代的未知的输入输出行为的进一步的样本。
英文摘要：
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.
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