m文件

Implementation of fuzzy system with matlab in case of "Choosing of Study".

程序1实现几个简单函数的响应序列； 程序2实现了两个不同序列的输出响应； 程序3给出了简单序列的零极点图以及输出响应。(Program a response sequence to achieve a few simple functions Procedure 2 implements two different sequences in response to the output Program 3 shows the output response of the pole-zero diagram and simple sequence)

波束形成中mvdr和music算法精确性对比分析，可对两个声源进行识别(Mvdr and music beamforming algorithm accuracy comparative analysis of the two sound sources can be identified)

Complex function fitting

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)

Constrained K-means Clustering with Background Knowledge算法实现(Constrained K-means Clustering with Background Knowledge algorithm)

matlab code for Viola Jones Object Detection

matlab中CORDIC算法用来开方，在0~2之间精度很高(matlab CORDIC algorithm is used in prescribing between 0-2 high precision)

这是有关MATLAB学习的一些编程技巧和使用的一个程序示例(MATLAB STUDY)

说明：  Steganograohy using LSB&DWT