xiandaikongzhililunheyingyong

灰色预测工具箱。 特性： 0 数据预处理，替换掉一些不符合条件的点：如连续相等的点 1 选择最好的c进行平移变换 2 残差太大的，剔除坏点 3 最后使用 新陈代谢 灰色模型求解（xcdxgm11） 函数： ccfx.m:残差分析 gm11.m:一般的灰色预测工具 xcdxgm11.m:新陈代谢灰色预测工具(Grey toolbox. Features: 1 Data preprocessing, replace the points that do not meet the criteria: If a continuous point equal to 2 to select the best translation transformation c 3 residuals too much, remove dead pixels 4 final model was solved using the metabolism of gray (xcdxgm11) Function: ccfx.m: residual analysis gm11.m: normal gray prediction tools xcdxgm11.m: metabolism gray prediction tools)

说明：  数字电子技术基本元件--触发器 的程序, 支持多种触发器图形显示.(Trigger)

基于遗传算法的车间布局优化MATLAB源码(MATLAB code optimization based on genetic algorithms workshop layout)

说明：  双馈式风机并网谐波控制研究，用于多台DFIG并网研究转子侧、电网侧、并网点谐波(Research on Harmonic Control of Doubly-fed Fan Connected to Grid. It is used to study harmonic waves on rotor side, grid side and grid point of multiple DFIG grid connection.)

DES的MATLAB文件。。。有很好的操作界面(use matlab to achieve des)

pitch=CorrelogramPitch(correlogram, width [, sr, lowPitch, highPitch]) computes the pitch of a correlogram sequence by finding the time lag with the largest correlation energy. (pitch = CorrelogramPitch (correlogram. width [02:04 lowPitch. highPitch]) computes the pitch of a correlogra m sequence by finding the time lag with the large st correlation energy.)

Particle Swarm Optimization Protocol

信道编码的相关源码,包括检错码,线性分组码,卷积码,Turbo码和低密度校验码(LDPC).(Source, channel coding error detection code, linear block codes, convolutional codes, Turbo codes and low density parity check code (LDPC).)

Transmit Beamforming for MIMO Communication Systems with Low Precision ADC at the Receiver

Function to perform Principle Component Analysis over a set of training vectors passed as a concatenated matrix. Usage:- [V,D,M] = pca(X,n) [V,D] = pca(X,aM,n) where:- <input> X = concatenated set of column vectors aM = assume that the mean is aM n = number of principal components to extract (optional) <output> V = ensemble of column eigen-vectors D = vector of eigen-values M = mean of X (optional)