MPLAB_IDE

电力电子、电机控制系统仿真模型 经典仿真源程序 洪乃刚版本(Power electronics, motor control system simulation model version of the classic simulation source Hong Naigang)

基于虚拟现实工具箱创建的仿人机器人平台，在该平台上结合matlab仿真工具箱可以进行机器人的控制研究。(Virtual Reality Toolbox to create a humanoid robot platform, the platform can be combined with matlab simulation toolbox robot control.)

Matlab M-Files:There are four ways of doing code in Matlab. One can directly enter code in a terminal window. This amounts to using Matlabas a kind of calculator, and it is good for simple, low-level work. The second method is to create a script M-fi le. Here, one makes a fi le with the same code one would enter in a terminal window. When the fi le is “run”, the script is carried out. The third method is the function M-fi le. This method actually creates a function, with inputs and outputs. The fourth method will not be discussed here. It is a way to incorporate C code or FORTRAN code into Matlab this method uses .mex fi les. We will not disucss it here.

public guideness for swarm Ops in Matlab

sinusoidal pulse width modulation (pwm) inverter

说明：  基本遗传算法maltab源程序， 比较实用(Maltab basic genetic algorithm source code, more practical)

this file contain the matlab code and source code file .it is used for separation of lung sound from heart sound

Ising Model is described here in primary manner. The other versions are upcomming..

FDD链路的仿真使用的是KA波段，其中在接收端涉及到多种算法（位同步算法，DD算法等）(FDD link simulation using KA band)

How the K-mean Cluster work Step 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (N-k) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3 . Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4 . Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments. (How the K-mean Cluster workStep 1. Begin with a decision the value of k = number of clusters Step 2. Put any initial partition that classifies the data into k clusters. You may assign the training samples randomly, or systematically as the following: Take the first k training sample as single-element clusters Assign each of the remaining (Nk) training sample to the cluster with the nearest centroid. After each assignment, recomputed the centroid of the gaining cluster. Step 3. Take each sample in sequence and compute its distance from the centroid of each of the clusters. If a sample is not currently in the cluster with the closest centroid, switch this sample to that cluster and update the centroid of the cluster gaining the new sample and the cluster losing the sample. Step 4. Repeat step 3 until convergence is achieved, that is until a pass through the training sample causes no new assignments.)