garchkit_20041121

Kernel principal component analysis (kernel PCA) [1] is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping.

说明：  支持向量机用于分类问题的演示程序，分线性与非线性两部分(support vector machines for the demonstration classification procedures, sub-linear and nonlinear part 2)

基于matlab2011b，接三相独立负载，给需要的朋友分享。(Based matlab2011b, connected three independent load to the need to share.)

用龙格库塔法求得微分方程的数值解；用龙格库塔法求得微分方程的数值解(Obtained by using Runge-Kutta numerical solution of differential equations obtained by using Runge-Kutta numerical solution of differential equations)

ecg signal processing with alot of classification and feature extraction technique

给出利用S函数进行Simulink仿真时，调用.mdl文件的接口函数，每一步均给出了相应的注释说。(Give the Simulink simulation using the S function, interface function call of.Mdl files, each step are given in notes.)

这个一个学习自适应滤波的好程序，里面有很多例子，可以供大家学习参考。(This a good learning process adaptive filtering, there are many examples we can learn for reference.)

matlab对aloha，s-aloha，csma的仿真(matlab pairs of aloha, s-aloha, csma Simulation)

在matlab下利用空间均值法、中值法、最大值法和最小值法实现数字图像的平滑处理(Use of space under the matlab mean, median method, the maximum and minimum method method of smoothing digital images)

matlab中绘图的一些精彩例子介绍及相关的代码参考。(Some wonderful example introduces the drawing and the related code in matlab.)