Part_4_Modeling_and_Simulation

CurveLab-1.0工具箱，附带一些demo(CurveLab-1.0 toolbox，with some demo)

迭代学习控制：闭环以及开环控制器间切换，具有PI，D型等多型控制器，具有初态学习模块。(Iterative learning control: Closed-loop and open-loop controller to switch between, with PI, D-type and other type of controller with initial state learning modules.)

这是一个pca提取特征的小程序，是应用matlab语言进行编写的，适用于初学者。(This is a small programs characteristics extracted pca this is a small characteristics extracted pca program, application of matlab language is written, it is suitable for beginners. )

Phasor Plot PSCAD using Matlab

基于matlab的一种图片加密算法，针对*.jpg格式的图片，稍加修改亦可以加密.png/.bmp等各种类型的图片。(a kind of encryption dealing with pictures in format .jpg based on matlab.)

iris recognition using haar wavelet

对于两种目标类型，有无脉冲积累的SNR相对检测距离的曲线。(For both target types, with or without pulse accumulation of SNR relative detection distance curve.)

信号处理工具箱函数汇总，其中包括滤波器分析与实现函数、FIR 数字滤波器设计函数等。(Signal Processing Toolbox function summary, including analysis and implementation function filters, FIR digital filter design functions and so on.)

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.)

Adaptive Wavelet Thresholding for Image Denoising and Compression.pdf