Matlab3

说明：  在实验中，处理记录数据文件是经常要做的，而用Matlab来处理是我最推崇的，方便快捷，数据文件格式任意设置，均可处理，将数据文本data.txt,用Matlab来处理，用这个小程序做来非常容易，而且处理后得到的图形可直接Copy到各种需要的文件中。(In the experiment, record data processing documents often do, using Matlab to be dealt with me most respected, convenient and arbitrary data file format settings can be handled data.txt text data using Matlab to deal with the use of such small programs very easy to do, but after the Copy graphics directly to the needs of the document.)

对二滩水电站进行的simulink仿真，此仿真系统为单机无穷大系统，其中发电机控制部分为实际运行中数据。(Simulink simulation of two hydroelectric power station, the simulation system for single machine infinite bus system, the generator control part is data in practical operation.)

用蒙特卡洛法模拟编写的计算粒子的多次散射和单次散射的比较(Comparison of multiple scattering method using Monte Carlo simulation calculations prepared particles and single-scattering)

使用矩量法计算微带天线的特征阻抗，用户需自行更改其中的天线的几何尺寸和工作频点．(Calculated using the method of moments microstrip antenna characteristic impedance, the user need to change one of the antenna geometry and frequency.)

CDMA 2000 IMT Multi‑ Carrier (IMT‑ MC)

MIMO BFSK Modulation for digital signal processing

mat lab simulink pv model photovoltaik solar cell model gunes

基于SVR的非线性系统故障诊断，假设非线性系统在1.4s~1.6s期间发生系统参数变化故障(SVR-based fault diagnosis of nonlinear systems, assuming nonlinear systems in 1.4s ~ 1.6s failure occurred during the parameter variations)

2个服务台，顾客流服从泊松分布的排队论仿真模拟(2 help desk, customer flow simulation of queuing theory to obey the Poisson distribution)

七单元天线阵MUSIC DOA估计： d=1 ， 天线阵元的间距； lma=2， 信号中心波长； 四输入信号； A=[A1,A2,A3,A4]， 得出A矩； 四信号的频率d=[1.3*cos(v1*n) 1*sin(v2*n) 1*sin(v3*n) 1*sin(v4*n)] 构造输入信号矢量 U=A*d 总的输入信号 总输入信号的协方差矩阵 [s,h]=eig(c) 求协方差的特征矢量及特征值 取出与零特征值对应的特征矢量 求协方差矩阵的逆矩阵 应用Music法估计输出 绘出各波达方向图(Seven-element antenna array MUSIC DOA estimates: d = 1, Antenna Array pitch LMA = 2 signal center wavelength four input signals A = [A1, A2, A3, and A4], drawn A moment tetra-frequency of the signal D = [1.3* cos (V1* n) 1* ⁢ sin (v2* n) 1* sin (v3* n) 1* sin (V4* n)] constructed input signal vector U = A* D of the total input signal of the total input signal covariance matrix [S] = EIG (c) seeking covariance feature vector and the feature value removing and corresponding to the zero eigenvalues ​ ​ characterized vector seeking covariance matrix inverse matrix Applications Music estimate output plotted DOA Figure)