GSO2

可进行电力系统多节点系统的优化潮流计算，适合电力行业同仁仿真用(Power system can be multi-node system optimal power flow calculation for the power industry using simulation Tongren)

混沌序列发生器，一种随机序列的预测！时间序列的分析和预测！(Chaotic sequence generator, a random sequence of the forecast! Time series analysis and forecasting!)

这是一个通信系统的仿真，是用SIMULINK的S-FUNCTION方式编写的，共有20个M文件，1个DOC文件，本系统的仿真设计到编码、调制、IFFT、上下变频、高斯信道建模、FFT、PAPR抑制、各种同步、解调和解码等模块，每个模块都用详细的代码和注释，并通过了系统性能的仿真验证了系统设计的可靠性，可以直接拿来仿真整个系统，也可来学习各个部分的设计。(This is a communication system simulation, is the S-FUNCTION SIMULINK approach to the preparation, a total of 20 M file, a DOC file, the system simulation design to coding, modulation, IFFT, and down conversion, Gaussian channel model , FFT, PAPR suppression, various synchronization, demodulation and decoding modules, each module with a detailed code and comments, and system performance through simulation and verification of the reliability of system design, simulation of the entire system can be directly used, also to learn the various parts of the design. )

A user guide for classification toolbox using Matlab

用matlab统计不同距离的RSSI值，画折线图，并最将各距离处平均值用最小二乘法拟合成3到5项多项式曲线(RSSI value of the matlab statistics at different distances, draw a line chart, and most average each distance with the least square method were fitted polynomial curve of 3-5)

用matlab实现扩展Informax算法与固定点算法Faast ICA，可对混合后信号进行独立分量分离 已通过测试。 (Using matlab to expand Informax algorithm with fixed-point algorithm Faast ICA, independent component separation of the mixed signal has been tested.)

自适应信号处理，RLS递归最小二次方算法(Adaptive signal processing, RLS recursive least square algorithm twice)

数字语音处理及MATLAB仿真[张雪英][程序源代码](source code of Digital Speech Processing written by xueying zhang)

说明：  三篇cs算法经典文章，cs算法是合成孔径雷达成像处理的常用算法之一。(3 cs algorithm classic article, cs algorithm is synthetic aperture radar imaging of the common algorithm.)

用MCE算法训练分类器，用Train.txt里的数据进行训练，用Test.txt的数据进行性能检测(Use MCE training algorithm of MCE to train the classifier, use the training data in Train.txt to train the classifier and use the data in the Test.txt to test the performance of the classifier )