Hamming_BER

支持向量机辅助程序，可以帮助完成支持向量机的计算过程(Support vector machine aids, can help to complete the calculation process SVM)

用matlab语言实现Butterworth滤波器的算法，经过调试可以运行！(using Matlab language Butterworth filter algorithm, After debugging can run!)

Shirmohammadi load flow

仿真产生OFDM信号；通过信道；实现解调。子载波用16QAM调制。(Simulation generate OFDM signals through the channel achieve demodulation. Subcarriers with 16QAM modulation.)

基于kaiser窗的双谱线插值FFT谐波分析，在MATLAB中求图像纹理特征，一些自适应信号处理的算法。( Dual-line interpolation FFT harmonic analysis kaiser windows, In the MATLAB image texture feature, Some adaptive signal processing algorithms.)

本代码在OFDM系统中，用matlab实现8-PSK的调制、载波同步及其误码率计算(This code completes The modulation，carrier synchronization and the bit error rate test of 8-psk in an OFDM system .)

Statistical interference book.

数学建模与数学实验（第3版），各种常用的算法，ppt，M文件，各算法后附有matlab程序，可以方便的运行。(Mathematical modeling and mathematical experiment (3), a variety of commonly used algorithms, ppt, M file attached to each algorithm matlab program, easy to run.)

中值滤波窗口大小影响滤波器性能,3×3滤波窗口可以很好地保持图像细节。提出一种新的自适应中值滤波方法。将3×3窗口中心的极值点作为候选噪声点,若候选噪声点仍然是7×7窗口的极值点,则该点即是噪声点。若以噪声点为中心的3×3滤波窗口的中值不是噪声,则噪声用中值替换。重复以上过程,直到没有噪声点被替换。(Median filter window size affect the filter performance, 33 filter window can be well preserving image detail. Propose a new adaptive median filtering method. The 33 extreme point of the center of the window as the candidate noise the candidate noise is still 77 extreme points of the window, the point is that the noise point. Terms of noise centered on the point 33 filtering window value is not a noise, the noise is replaced with the median. The process is repeated until there is no noise point is replaced.)

分析了支持向量回归机在能源需求预测中的优势,确定了输入向量集合和输出向量集合,建立了基于Matlab技术的SVR能源需求预测模型.对我国1985-2008年能源需求相关数据进行模拟与仿真,并对中国2010年和2020年能源需求量进行预测.研究结果表明:一是中国未来对能源的需求量逐渐增加,从2010年的330400万吨标准煤上升到2020年418320万吨标准煤,年均增长率为2.39％；二是在解决我国能源系统小样本.非线性及高维模式识别问题中SVR比BP神经网络等方法有更高的预测精度.(Support vector regression analyzes advantage in energy demand forecast to determine the set of input vectors and output vector set, established SVR energy demand forecasting model based on Matlab technology. Energy demand for our 1985-2008 related data modeling and simulation , and China in 2010 and 2020 energy demand forecast results show that: First, China s increasing demand for energy in the future, from 3.304 billion tons of standard coal in 2010 rose to 4.1832 billion tons of standard coal in 2020, average annual growth rate of 2.39 second is to solve our energy system small sample nonlinear and high dimensional pattern recognition problem SVR higher prediction accuracy than the BP neural network method.)