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Example: Modeling an Inverted Pendulum in Simulink

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.)

说明：  脉内线性调频、脉间频率步进信号的模糊函数(Linear FM pulse, pulse frequency step signal ambiguity function)

基于matlab的区域增长算法，能够有效地实现区域增长，实现快速的计算(Matlab-based regional growth algorithm, can effectively realize the regional growth, achieve rapid calculation of)

load recieves maximum power from the source when its resitive value is equal to the thevenin resistance of the circuit.

matlab simulink model for pmsg wind ready model....

saleh无记忆功率放大器模型matlab仿真

应用matlab 分析机械力学中的工程振动问题,实例2。(Matlab applications in the engineering mechanics analysis of vibration problems, Example 2.)

实现粒子群算法的pid整定，适应于simulink模型的仿真，效果不错(Realize the pid tuning of particle swarm optimization algorithm, adapt to the simulation of simulink model, the effect is good)

说明：  针对一般的粒子群优化(PSO)学习算法中存在的容易陷入局部最优和搜索精度不高的缺点,对改进型PSO算法进行研究。由于惯性权重系数ω对算法是否会陷入局部最优起到关键的作用,因此,通过改变惯性权重ω的选择,对惯性权重系数采取线性减小的方法,引入改进型的PSO算法。采用改进的PSO算法对PID控制器进行参数优化并把得到的最优参数应用于控制系统中进行仿真。仿真实验结果表明:改进型PSO算法不会陷入局部最优,能得到全局最优的PID控制器的参数,并使得控制系统的性能指标达到最优,控制系统具有较好的鲁棒性。(Aiming at the shortcomings of general PSO learning algorithm, which is easy to fall into local optimum and low search precision, the improved PSO algorithm is studied)