We present a new method for the identification of linear time-invariant passive systems from noisy frequency response data. In particular, we propose to fit a parametrized port-Hamiltonian (pH) system, which is automatically passive, to supplied data with respect to a least-squares objective function. In a numerical study, we assess the accuracy of the resulting identified models by comparing our method to two other frequency domain system identification methods. One of the methods being compared is a recently published identification procedure that also computes pH systems and the other one is the well-known vector-fitting algorithm, which provides unstructured models. The numerical evaluation demonstrates a substantial increase in accuracy of our method compared to the other pH identification procedure and a slightly improved accuracy compared to vector-fitting. This underlines the suitability of our method for the estimation of passive or pH systems - in particular from noisy frequency response data.

中文翻译：

---

从噪声频率响应数据识别哈密尔顿港系统

我们提出了一种从噪声频率响应数据中识别线性​​时不变无源系统的新方法。特别是，我们建议将参数化的哈密顿 (pH) 系统拟合为自动被动的，以提供关于最小二乘目标函数的数据。在数值研究中，我们通过将我们的方法与其他两种频域系统识别方法进行比较来评估所得识别模型的准确性。正在比较的一种方法是最近发布的识别程序，它也计算 pH 系统，另一种是众所周知的矢量拟合算法，它提供非结构化模型。数值评估表明，与其他 pH 识别程序相比，我们的方法的准确性显着提高，与矢量拟合相比，准确性略有提高。这强调了我们的方法用于估计被动或 pH 系统的适用性 - 特别是从嘈杂的频率响应数据。