kNearestNeighbors

说明：  自适应中值滤波器的滤波方式和传统的中值滤波器一样,都使用一个矩形区域的窗口Sxy,不同的是在滤波过程中,自适应滤波器会根据一定的设定条件改变(即增加)滤窗的大小,同时当判断滤窗中心的像素是噪声时,该值用中值代替,否则不改变其当前像素值,这样用滤波器的输出来替代像素(x,y)处(即目前滤窗中心的坐标)的值。(Adaptive Median Filter for filtering methods and the same as the traditional median filter, use a rectangular area of the window Sxy, different in the filtering process, the adaptive filter in on certain Presumed Conditions change (an increase of ) filter window size, while when the judge is the filter window center pixel noise, the median value is used instead of, or does not change its current pixel value, so that the output with a filter to replace the pixels (x, y) Office (that is, coordinates of the current filter window centered) values.)

GearDesign returns the basic triangulated model of a 3D spur Gear. It requires as input a few standard parameter to insert in the Gear structure.

Monte-Carlo Integral

psat2.1.9-mat 最新版,很好的，可以用在matlab2014a中(psat2.1.9-mat new version,very good，using of matlab2014a)

这是时频分析的MATLAB源码 xi wang dui gao xinhao chu li de tong zhi you bangzhu(xi wang dui gao xinhao chu li de tong zhi you bangzhu)

三相SPWM整流电路simulink仿真原代码(Three-phase SPWM rectifier circuit simulink simulation source code)

some neural network rules Matlab code such as:perceptron, delta, widrow exist in this document

维也纳大学LTE-A系统级仿真matlab程序。(University of Vienna LTE-A system-level simulation matlab program.)

用MATLAB实现自适应神经模糊推理系统（ANFIS）结构训练。代码中，首先创建一个初始原ANFIS结构，然后采用遗传算法（GA）、粒子群优化（PSO）来训练ANFIS。此进化训练算法可用于解决非线性回归函数逼近问题。(Implementation of adaptive neural fuzzy inference system (ANFIS) based on MATLAB. Code, the first to create an initial original ANFIS structure, and then using the genetic algorithm (GA), particle swarm optimization (PSO) to train ANFIS. This evolutionary training algorithm can be used to solve the nonlinear regression function approximation problem.)

模糊c-均值聚类算法通过优化目标函数得到每个样本点对所有类中心的隶属度，从而决定样本点的类属以达到自动对样本数据进行分类的目的，一般用于故障识别与分类。(Fuzzy c- mean clustering algorithm obtains the membership degree of every sample point to all class centers by optimizing the objective function, and determines the classification of the sample points to achieve automatic classification of sample data. It is generally used for fault recognition and classification.)