MATLAB6.x

opracowanie wykresów i obliczeń do przesuwnia fazy

labview 调用MATLAB的DLL的例子 ，很有参考作用 (MATLAB calls the DLL labview examples of useful reference)

最优化的MLC相干接收机mattlab程序源码！ (Optimal MLC to coherent the receiver mattlab program source code!)

matlab about compression

基于波形分集的雷达抗距离欺骗干扰方法（二）的仿真，仿真采用了简单的线性调频信号和时域脉压(Radar distance deception jamming resistance method based on waveform diversity (2) simulation, the simulation using the simple linear frequency modulation signal and the time domain pulse pressure)

基于散射模型的PolSAR滤波算法（J.S.Lee），matlab程序(Scattering model PolSAR based filtering algorithm (JSLee), matlab program)

说明：  学习C调用Mathematica内核函数的一个例子,包括计算和图形接口.(Learning C calling Mathematica kernel functions with examples, including computing and graphics interfaces.)

罗盘图，主要解决界面处理中遇到的罗盘画法，用MATLAB相对C语言简单(Compass map, mainly to solve the interface treatment encountered in compass drawing, using MATLAB relatively simple C language)

HMM的范例程序，包含特征样本和产生训练数据的程序，注释详细，适合参考学习(HMM sample program, including feature samples and generator training data, detailed notes for reference study)

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)