DigModTutorial

MATLAB提供了强大的计算和图形功能，通过MATLAB编程实现蚁群算法求解TSP问题(MATLAB provides a powerful computing and graphics capabilities, through the MATLAB programming ant colony algorithm for TSP problem)

matlab读取dbf格式文件，使用成功可以。(matlab read dbf format file)

基于线性预测分析的语音信号压缩感知.PDF(Compressed sensing for speech signal based on linear prediction analysis)

用ODE方法，采用PID控制方法设计控制器，对电机进行仿真。(With ODE method using PID control design of the controller, the motor simulation.)

这编程是关于三维图形的仿真，X.Y都是有取值范围的，图形都是有各种颜色(This program is about three-dimensional graphics simulation, XY are all the range, the graphics are a variety of colors)

Fuzzy Mutual information which is used to calculate the mutual information between two variables.

this contains simulated model of 3 phase inverter in 180 degree mode

考虑L的三个不同值：L＝256（3个数据段），L＝128(7个数据段)和L=64（15个数据段）。各自的谱估计图如上图所示。可以明显的看到，加窗明显的减小了频谱上的假谱峰，但也更加进一步平滑了谱峰。所以，对于L＝64的情况，在ω＝0.8π的谱线可以很确定的辨认，但是那两个靠近的谱峰不容易区分。对于L＝128的情况，这种情况提供了在分离和检测间最好的均衡。当然，对于在L＝256时的情况，效果是更好的，能够从谱估计图上明显的分辨出三条谱线的存在以及它们幅度关系的强弱。 除了Welch法外，还可以采用对多个周期图求平均的功率谱估计方法的其他方法如Bartlett法等等，在功率谱估计上也能取得较好的结果。(consider three different values : L = 256 (3 data), L = 128 (7 data) and L = 64 (15 data). Their spectral estimation map as shown in Fig. It is clear to see that increase significantly decreasing window of the spectrum of false peaks, but much more to further smooth the peaks. Therefore, L = 64, in the case = 0.8 spectrum can determine the identification, but that the two peaks around is not easy to distinguish. L = 128 for the situation, which provided the separation and detection among the best balanced. Of course, L = 256 in the case, the effect is better, from the spectral estimation map clearly distinguish between the three lines of the existence and their relationship to the rate of strength. In addition to Welch law, but also can be used to map multiple cycle for the average power)

说明：  一种最小二乘法的RBF网络 ---------------------------------- radical basis function neural network ------------------------------------------ clear all close all global sp k w2 b2 unitcenters alldatanum=400 traindatanum=300 testdatanum=100 sp=8 errorlimit=0.1 u=rand(1,alldatanum+10) y=zeros(1,alldatanum+10) for i=4:alldatanum+3 y(1,i)=1.5*y(1,i-1)*y(1,i-2)*y(1,i-3)/(3+y(1,i-1)+y(1,i-2)*y(1,i-2)+... y(1,i-3)*y(1,i-3)*y(1,i-3))+u(1,i)+u(1,i-1)+u(1,i-2) 。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。(radical basis function neural network ------------------------------------------ clear all close all global sp k w2 b2 unitcenters alldatanum=400 traindatanum=300 testdatanum=100 sp=8 errorlimit=0.1 u=rand(1,alldatanum+10) y=zeros(1,alldatanum+10) for i=4:alldatanum+3 y(1,i)=1.5*y(1,i-1)*y(1,i-2)*y(1,i-3)/(3+y(1,i-1)+y(1,i-2)*y(1,i-2)+... y(1,i-3)*y(1,i-3)*y(1,i-3))+u(1,i)+u(1,i-1)+u(1,i-2) 。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。。)

Subcarrier allocation in mimo-ofdma