System-Identification

这是Matlab的中文实用手册，内容很齐全，对学习和开发Matlab有很重要的帮助，找了老半天才找到这样齐全的文档，又需要的可以下载。(This is a practical handbook of Chinese Matlab, which is very important of learning and the development of Matlab,this is not easily fined, also in need can be downloaded)

针对20个已知类别数据，前15个训练样本和带刺的5个样本，通过该程序可识别出待测的样本属于哪一类。(Data for the 20 known types, the first 15 training samples and barbed 5 samples can be identified by the program which type of samples tested.)

说明：  在MATLAB平台上，编译自适应算法的M文件，进行系统仿真，并对滤波器的阶数、收敛步长、跟踪速度和稳态误差进行了详细的分析和研究。( On the MATLAB platform, the M file is written and system simulation experiment is put on. The main parameters such as number of filter exponent, step-size, track-speed, errors and so on are discussed, that provided a solid foundation for the hardware realization of adaptive filters.)

说明：  雨流计数法，可以参考，用于雨流计数统计循环次数(Rain flow counting method)

用逼近法计算变形的第二类整数阶贝赛尔函数值(Used approximation method to calculate the deformation of the second type of integer-order Bessel function values)

基于Radon变换提出了一种新的图像识别方法，首先构造了二维图像在Radon变换空间的平移和比例 不变量，然后根据变换空间数据的特性，利用奇异值分解得到了旋转不变量并将其用于三类飞机的识别，仿真结果表明该方法具有很好的性能和较高的实用价值。(I nt hisp aper,a n ewi mager ecognitionm ethodb asedo nR adont ransform isp roposed,2一Di magem omentin variant basedo nR adontr ansformi sc onstructed.A ccordingto t hed atain t hetr ansforms pace,Si ngularV alueD ecomposition(SV D)i s proposedh ereto o btainr otationi nvariant.Si mulationr esultsa reg ivent oo btaini nsightin tot hee fficiencieso fferedb yo urm ethod)

说明：  基于MATLAB实现图像线性量化的源码，通过输入量化参数实现不同等级量化(Based on MATLAB to achieve linear quantitative image source, by entering the quantization parameters to achieve different levels of quantization)

MATLAB Codes for Calculate the line of sight Channel Gain

1. 按照式计算相似性矩阵 2. 排除自身的相似度 3. 按照式计算归一化矩阵 4. 按照式计算归一化拉普拉斯图矩阵L 5. 计算L的特征向量，将前个特征值最大的向量按列放置成一个矩阵，即依次为前个特征值最大的特征向量 6. 归一化X形成矩阵Y 7. 对矩阵Y按每行为一个数据点，进行k-means聚类，第行所属的类就是原来所属的类。(1. similarity matrix based on formula computation 2. rule out your own similarity. 3. normalized matrix based on formula computation 4. normalized formula Laplasse matrix L according to formula. 5. Calculate the eigenvectors of L. Place the vectors with the largest eigenvalues in columns into a matrix, that is, the eigenvectors with the largest eigenvalues in turn. 6. normalized X formation matrix Y 7. K-means clustering is applied to matrix Y according to one data point per behavior. The first row belongs to the original class.)

说明：  matlab图像处理的教学视频，详细介绍了视频的处理(matlab image processing, instructional videos, video processing described in detail)