BPSK

Uses the level set method with reinitialization to compute the movement of fronts under a given velocity field.

SMT算法的求解器程序，用来计算光纤模式等，十分好用(The program is used to calculate the fiber mode. )

iteration de gauss dans le calcul du champ matlab modelisation electromagnétique les différence finies

经典的多点法形状复原算法中的2点法的程序。本程序用频域法和积分法2种方法复原了同一形状。数据则由csv文件读入。(classic multi-point method shape the recovery algorithm 2:00 Act procedures. The procedure uses frequency-domain law and integration methods to recover two of the same shape. Data from csv file into.)

本资源是matlab的进行间接平差运算的源代码(This resource is the indirect adjustment calculation matlab source code)

Matlab教程PPT，Matlab概述与应用基础(Matlab PPT)

视频的编码和解码过程的简单仿真，输入图像是一幅RGB图像，对其的编码过程都尽量模拟模拟视频的方式(Video encoding and decoding process of a simple simulation, the input image is a RGB image, the encoding of their simulation process as far as possible the way analog video)

自适应滤波算法的一部分源程序，及一些绘图程序(failed to translate)

该程序源代码实现了matlab通信系统性能的仿真，调制方式QPSK仿真(The source code implements communication system simulation matlab, modulation QPSK simulation)

说明：  最速下降法是迭代法的一种,可以用于求解最小二乘问题(线性和非线性都可以)。在求解机器学习算法的模型参数，即无约束优化问题时，梯度下降（Gradient Descent）是最常采用的方法之一，另一种常用的方法是最小二乘法。在求解损失函数的最小值时，可以通过梯度下降法来一步步的迭代求解，得到最小化的损失函数和模型参数值。反过来，如果我们需要求解损失函数的最大值，这时就需要用梯度上升法来迭代了。在机器学习中，基于基本的梯度下降法发展了两种梯度下降方法，分别为随机梯度下降法和批量梯度下降法。(The steepest descent method is a kind of iterative method, which can be used to solve the least squares problem (both linear and nonlinear). In solving the model parameters of machine learning algorithm, that is, unconstrained optimization, gradient descent is one of the most commonly used methods, and the other is the least square method. When solving the minimum value of loss function, the gradient descent method can be used step by step to get the minimum value of loss function and model parameters. Conversely, if we need to solve the maximum value of the loss function, then we need to use the gradient rise method to iterate. In machine learning, two kinds of ladders are developed based on the basic gradient descent method)