Matlab_Wireless_Communications_samples

说明：  神经网络学习二维非线性函数 ,多维非线性辨识的MATLAB程序(neural network learning 2D nonlinear function, multi-dimensional nonlinear identification procedures MATLAB)

说明：  本书中包含有 所有MATLAB的函数中文简介 对英文不好的朋友 有很好的用处(This book contains all the MATLAB functions Introduction to the English is not good Chinese friends have a good use)

利用matlab进行主成分分析，得到各个相关系数等(Principal component analysis using matlab to get the various correlation coefficients, etc.)

L = vgg_line3d_from_lP_lin(s,P [,imsize])

AUTOSAROS规范研究及其在uC/OS中实现,说明了汽车软件规范AUTOSAROS(AUTOSAROS normative research and its implementation in uC/OS, the description of the car software specifications AUTOSAROS)

大规模MIMO的4*4天线矩阵情况下ZF（zero forcing）检测算法的实现(The realization of large-scale MIMO 4x4 antenna matrix case ZF (zero forcing) detection algorithm)

Simple demonstration of modeling intermodulation tones.

采用matlab编写的数值方法中的案例，标号为书中的例题(Numerical methods using Matlab prepared case, the label for the examples in the book)

说明：  High Capacity Secure Image Steganography Based on

MATLAB 牛顿下山法的一个小程序，原理就不详细说了。因牛顿迭代法受初值选取的限制，为防止迭代发散，对迭代过程再附加一项要求:|f(x(k+1))|<|f(x(k))|,将牛顿法迭代的结果:x(k+1) =x(k)-f(x(k))/f (x(k))和前一近似值x(k)适当加权平均做为新的改进值:x(k+1)=u*x(k+1) +(1-u)*x(k),其中u(0<> 迭代时u取1开始，逐次减半计算，直至附加要求符合为止。实例计算中x(k)=x(0)不变，只更新u和x(k+1)，直至:|f(x(k+1))|<|f(x(k))|（即|f(x(1))|<|f(x(0))|)，然后更新下山因子为u=1,继续以牛顿法迭代。(MATLAB Newton-down a small program, the principle is not explained in detail. Newton iteration by the initial value of the selected limit, in order to prevent additional iterative divergence, the iterative process a request: | f (x (k+1)) | < | f (x (k)) | Newton iteration Results: x (k+1) ' = X (k)-f (x (k))/f' (x (k)) and a previous approximation x (k), appropriately weighted average as a new improved value: x (k+1 ) = u* x (k+1) ' + (1-u)* x (k), where u (0 < u> iteration take successive calculated at 50 until the additional requirements found so far. examples of calculation of X ( k) = x (0) unchanged profile u and x (k+1, hungry) until: | f (x (k+1)) | < | f (x (k)) | (i.e. | f (x (1)) | < | f (x (0)) |), then profile downhill factor for u = 1, to continue to Newton iteration.)