Anisotropic diffusion filtering for signal smoothing as a low-pass filter has the advantage of the edge-preserving, i.e., it does not affect the edges that contain more critical data than the other parts of the signal. In this paper, we present a numerical algorithm based on least squares support vector regression by using Legendre orthogonal kernel with the discretization of the nonlinear diffusion problem in time by the Crank-Nicolson method. This method transforms the signal smoothing process into solving an optimization problem that can be solved by efficient numerical algorithms. In the final analysis, we have reported some numerical experiments to show the effectiveness of the proposed machine learning based approach for signal smoothing.

翻译：用于光滑信号的低通道过滤器的Anisotropic 扩散过滤器具有边缘保护的优点,即它不会影响比信号其他部分更关键的数据的边缘。在本文中,我们提出了一个基于最小方位的数值算法支持矢量回归,方法是使用Tullere orthogonal内核,用Crank-Nicolson方法及时将非线性扩散问题分解。这种方法将信号光滑过程转化为解决可以通过高效数字算法解决的优化问题。在最终分析中,我们报告了一些数字实验,以显示拟议的机器学习方法在信号平滑方面的有效性。