girth84r

精通Matlab教程，内容详细，每个命令有详细讲解和代码示例(Proficient in Matlab tutorials, detailed, each command has the detailed explanation and code examples)

Feature Extraction using matlab

MATLAB——数字信号处理与应用的源代码(MATLAB- digital signal processing and application source code)

Example of using fuzzy logic to control water level in bottle.

distributed in matlab mfile

此码为信号处理方面，干扰的抑制等，利用波束，分集器等算法(The code for signal processing, interference suppression, the use of the beam, diversity, etc. Algorithm)

FIR低通滤波器设计,表达得还比较详细，可以借鉴参考(FIR low-pass filter design, is also expressed in more detail, you can learn from the reference)

it is finite differeance time domain code for antenna

利用matlab对加速度传感器进行数据采集并计步。(This example shows how to collect acceleration data an Android device and use it to count the number of steps taken.)

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)