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PCA主成分分析算法用于人脸识别的例子。此算法是基于矩阵特征值的排序而提取出主要成分的。(PCA principal component analysis algorithm for face recognition example. This algorithm is based on the ranking of the matrix eigenvalue extracted principal components.)

Pearson 系数，是复杂网络 的一个重要参数(Pearson coefficient is a complex network of an important parameter)

一个关于基站发射功率多径衰落，阴影衰落以及多普勒频移等的matlab仿真(A base station transmit power multipath fading, shadow fading and Doppler frequency shift matlab simulation)

matlab编程 包括课件和配套的程序源码 非常适合自学。(matlab programming, including courseware and supporting source code is very suitable for self-learning process.)

数据矩阵运用非负矩阵分解算法，分解得到两个低维矩阵，并用于信号分离。(The data matrix is decomposed into two low-dimensional matrices by non-negative matrix decomposition algorithm and used for signal separation.)

说明：  比较详细的蚁群算法matlab程序，主要功能解决旅行商问题(Ant colony algorithm in more detail matlab program, the main function of Traveling Salesman Problem)

针对滚动轴承故障信号具有非平稳、非高斯的特点，提出了将时域分析与小波分析相结合的方法对滚动轴承进行故障诊断。在研究不同信号分析方法理论的基础上，以滚动轴承外圈故障振动信号为例，采用多种信号处理方法进行了分析。结果表明，各种分析方法在分析轴承故障时的特点各不相同，在实际使用中，可将时域分析与小波分析综合使用，实现轴承状态的实时监测与故障的准确定位。(For rolling bearing fault signals have non-stationary, non-Gaussian, we proposed the time-domain analysis and wavelet analysis method of combining the rolling element bearing fault diagnosis. In the study of different analytical methods based on the theory of signal to the outer ring rolling bearing fault vibration signal, for example, using a variety of signal processing methods are analyzed. The results show that a variety of analytical methods in the analysis of the characteristics of bearing failure vary, in actual use, can be time-domain analysis and synthesis using wavelet analysis, to achieve accurate positioning of the bearing condition monitoring and real-time fault.)

基于matlab的波束赋形优化算法，需要搭配cvx工具包使用，代码运行时间较长(The beam forming optimization algorithm based on MATLAB needs to match the CVX toolkit, and the code runs longer.)

用MATLAB做的解方程的程序源程序及演示课件。(do with MATLAB solution to the equation and demonstrate procedures source courseware.)

说明：  主元分析方法（PCA）是一种基于多元统计分析的数据降维方法, 它利用过程变量间的相关关系, 建立正常工况下的主元模型, 通过检验新的数据样本相对于主元模型的背离程度, 从而发现异常和故障。 (Principal Component Analysis (PCA) is based on multivariate statistical analysis of the data reduction method, which uses the correlation between process variables and establish the normal condition of the main element model, by testing samples of the new data relative to the principal component model departure from, and thus abnormal and failure.)