Short-term-energi

we have a photo camera with DC Motor and closed loop controller is equal k/s^2+s+k. first,we shoul find ZOH,then we identify the system with ARX model that na=nb=2 Here,we want to identify the Dynamic Linear System. we use different inputs such as normal distribution,uniform distribution,...

It tells you how to make a GA programme!(It is useful for a new learner to leaen GA!)

说明：  模拟退火应用的和序，含有gcp,tsp,isp,mcp(Application of simulated annealing and the sequence containing gcp, tsp, isp, mcp)

说明：  使用matlab仿真三维物点的透视投影成像(perspective projection )

SIMULINK中搭建了光伏发电电池模型，对不同的光照和温度产生不同的功率(SIMULINK to build the model of the photovoltaic cells produce different power for different light and temperature)

Robot arm rotation. Rotation of 3 axial platform, simple task but it was fun. Play with it do whatever you want. Enjoy!!

matlabGUI编程，实现平面图像的水平分割，显示原图像，分割后矩形图像及分割后矩形四个角点的坐标值(matlabGUI programming, horizontal split plane of the image, display the original image, the rectangular coordinates of the four corner points of the rectangular image after segmentation and segmentation)

A simple (de)compression program using Run Length Encoding. Compression is done that easy: compressedData = rle(originalData) Decompression: originalData = rle(compressedData) (A simple (de) compression program using Run Length Encoding.Compression is done that easy: compressedData = rle (originalData) Decompression: originalData = rle (compressedData))

gammatonegram of wav file usinf gammatone filter

聚类算法，不是分类算法。分类算法是给一个数据，然后判断这个数据属于已分好的类中的具体哪一类。聚类算法是给一大堆原始数据，然后通过算法将其中具有相似特征的数据聚为一类。这里的k-means聚类，是事先给出原始数据所含的类数，然后将含有相似特征的数据聚为一个类中。所有资料中还是Andrew Ng介绍的明白。首先给出原始数据{x1,x2,...,xn}，这些数据没有被标记的。初始化k个随机数据u1,u2,...,uk。这些xn和uk都是向量。根据下面两个公式迭代就能求出最终所有的u,这些u就是最终所有类的中心位置。(Clustering algorithm, not a classification algorithm. Classification algorithm is to give a figure, and then determine the data belonging to a specific class of good which category. Clustering algorithm is to give a lot of raw data, and then through the algorithm which has similar characteristics data together as a class. Here k-means clustering, is given in advance the number of classes contained in the raw data, then the data contain similar characteristics together as a class. All information presented in or Andrew Ng understand. Firstly, raw data {x1, x2, ..., xn}, the data is not labeled. K random initialization data u1, u2, ..., uk. These are the vectors xn and uk. According to the following two formulas can be obtained final iteration all u, u is the ultimate all these classes the center position.)