A novel method of jitter decomposition by 1 Dimension-convolutional neural network (1D-CNN) using jitter histogram points is proposed for decomposing in the time interval error (TIE) of oscilloscope. Unlike the traditional jitter decomposition method based on the dual-Dirac model and Gaussian mixture model (GMM), the proposed jitter decomposition can reduce complexity and improve accuracy. The proposed method consists of a four-layer 1D-convolutional layer and multi-layer perceptron (MLP). Experimental results show that the proposed approach can decompose the total jitter (TJ) into deterministic jitter (DJ) and random jitter (RJ) and is better than the traditional jitter decomposition method, i.e., GMM, Fast Fourier transform and time lag correlation (FFT+TLC). Besides, results show that the proposed method’s performance is better than 2D-PointCNN, PointRNN, CNN, and PointANN.

中文翻译：

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抖动分解遇到机器学习：一维卷积神经网络方法

针对示波器的时间间隔误差(TIE)，提出了一种利用抖动直方图点的一维卷积神经网络(1D-CNN)进行抖动分解的新方法。与传统的基于双狄拉克模型和高斯混合模型（GMM）的抖动分解方法不同，所提出的抖动分解可以降低复杂度并提高精度。所提出的方法由四层一维卷积层和多层感知器（MLP）组成。实验结果表明，该方法可以将总抖动(TJ)分解为确定性抖动(DJ)和随机抖动(RJ)，优于传统的抖动分解方法，即GMM、快速傅立叶变换和时滞相关(FFT)。 +薄层色谱）。此外，结果表明，该方法的性能优于 2D-PointCNN，