This paper focuses on the identification of the multiple-input multiple-output commensurate fractional order systems. Different from the assumption of known frequency domain data and prior information about noise in the general frequency domain identification algorithm, the reliable frequency domain data is measured in this paper by designing special excitation signals. Then, in order to suppress the impact of noise, the data used in algorithm are truncated from the low frequency band. In addition, this paper considers the case where the sampling data are disturbed by outliers, and a matrix decomposition method with threshold value is developed to eliminate the influence of outliers. After that, the parameters of the system to be identified are estimated by the frequency domain subspace identification method. The validity of the proposed method is demonstrated by an illustrative numerical example.

中文翻译：

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使用带异常值的时域数据对分数阶系统进行频域子空间识别

本文重点研究了多输入多输出相称分数阶系统的识别。与一般频域识别算法中对已知频域数据和噪声先验信息的假设不同，本文通过设计特殊的激励信号来测量可靠的频域数据。然后，为了抑制噪声的影响，算法中使用的数据从低频段截断。此外，本文还考虑了采样数据受到异常值干扰的情况，开发了一种带阈值的矩阵分解方法来消除异常值的影响。之后，通过频域子空间识别方法估计待识别系统的参数。