power-electronics

matlab program usin particle swarm optimisation method for anaylysing the convergence techniques

MATLAB中实现PID控制系统的，改变输入r(k）分别为阶跃信号、三角波、方波。(MATLAB to achieve PID control system, change the input r (k) are step signal, triangle wave, square wave.)

修正的music 算法仿真，MUSIC算法是用于做信号的DOA估计(modified music)

降压Buck电路闭环matlab仿真，分享供大家使用(Buck Buck circuit closed-loop simulation matlab share for everyone to use)

optimization conjugate

控制系统的PD校正设计及仿真源码PD control system design and simulation of source correction(Control System Design and Simulation of PD calibration source PD control system design and simulation of source correction)

ICA an optimization algorithm with matlab.

台湾人写的GFSK调制和解调，非常清晰，便于学习(Taiwanese write GFSK modulation and demodulation, very clear, easy to learn)

Statcom的matlab仿真，matlab2010版本，包含电压环和电流环，锁相等(Statcom matlab simulation, matlab2010 version, including voltage loop and current loop, lock equal)

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.)