capacitor_clamped

使用这个matlab程序，你可以对图像实现加高斯白噪声或者椒盐噪声(whth this matlab code，You can add Gaussian white noise or salt and pepper noise on the purpose image )

说明：  采用M语言进行卡尔曼滤波器仿真matlab程序(M language Kalman filter simulation)

Digital Signal Processing, developped in matlab short examples for processing

博弈论的相关工具程序,是个经济的工具箱，功能很多(Games)

SLToolKit Silverlight图片展示(SLToolKit Silverlight)

图像去噪 使用小波 curlet变换处理信号(The image denoising wavelet curlet transform)

这是一个求最短路的算法，用DIJKSTRA的方法(This is a shortest path algorithm for)

基于PCA的人脸识别代码，识别率可达88 (PCA-based face recognition code, the recognition rate up to 88 )

ART1自适应谐振网，已经通过测试，分类性能良好并且准确，此程序已经完成了运行，希望大家好好利用(ART1 adaptive resonance network, has passed the test, the classification performance is good and accurate, this program has completed running, I hope you make good use of)

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.)