breast-cancer

pca+svm源代码（matlab） matlab代码，pca进行特征提取，svm进行分类(pca+ svm source code (matlab) matlab code, pca for feature extraction, svm classification)

数学实验中用matlab完成的仿真实验，应用matlab的很多仿真功能(Mathematics experiment with matlab simulation experiments completed, many of the matlab simulation function)

利用SVM做回归线性测试。对于大盘指数的有效预测可以从整体上观测股市的变化提供强有力的信息，所以对上证指数的预测很有意义。通过对上证指数从1990.12.20-2009.08.19每日的开盘数进行回归分析，最终拟合的结果是：均方误差MSE=2.35705 e-005，平方相关系数 R=99.9195 。SVM的拟合结果还是比较理想的。(Make use of SVM linear regression testing. For the market index can predict the observed changes in the stock market as a whole to provide a strong information, so it makes sense to predict the Shanghai index. Through the Shanghai Composite Index opened daily from 1990.12.20-2009.08.19 number of regression analysis, the results of the final fit are: mean square error MSE = 2.35705 e-005, the square of the correlation coefficient R = 99.9195 . SVM fitting result is quite satisfactory.)

时间域建模，matlab提供了三种时间域建模函数，在这个压缩包里面分别叙述。(Time domain modeling, matlab provides three time-domain modeling functions, in this compressed packet which are described separately.)

粒度分析，显示累计图，返回特征值，很实用(Particle size analysis, chart shows the cumulative return to the eigenvalues)

该程序是用ＭＡＴＬＡＢ语言编写的符合３ＧＰＰＰ标准的ＴＵＲＢＯ编码内交织器．(that the procedure was used to prepare the MATLAB with 3GPPP Turbo coding standards within intertwined device.)

利用JADE算法实现盲源信号的分离，JADE算法是盲源信号分离的常用算法(Using JADE algorithm for blind source signal separation, JADE algorithm is a commonly used algorithm of blind source signal separation )

说明：  实现利用高斯分布聚类，即完成em算法，直接在matlab上运行，可以作图直观显示（二维数据）(Achieve Gaussian distribution clustering, complete em algorithm, running directly on matlab, you can visualize mapping (two-dimensional data))

The files contain good ebooks for C++ programing and for Matlab

单级倒立摆二级倒立摆simulink model and mutiple control aglorithms Full order observer Full order observer with Kalman filter Poleß Placement method LQR optimization(Single-stage inverted pendulum inverted pendulum simulink model and mutiple control aglorithms Full order observer Full order observer with Kalman filter Poleß Placement method LQR optimization)