CG-SM

这是一个完整的数据传输过程的matlab例程!(This is a complete data transmission process Matlab routines!)

元胞自动机：森林火灾模型 规则： (1)正在燃烧的树变成空格位； (2)如果绿树格位的最近邻居中有一个树在燃烧，则它变成正在燃烧的树； (3)在空格位，数以概率p生长； (4)在最近的邻居中没有正在燃烧的树的情况下树在每一时步以概率f(闪 电)变为正在燃烧的树。( Cellular Automata: Forest Fire model Rule: (1) burning of trees into a space bit (2) If the tree can notably nearest neighbor has a tree in the burning, then it turned into being burning tree (3) in the space bit, a few with probability p growth (4) In the nearest neighbor is not burning under the tree the tree in each time step with probability f (flash electricity) Variable for the burning trees.)

UKFfunction [x,P]=ukf(fstate,x,P,hmeas,z,Q,R) UKF Unscented Kalman Filter for nonlinear dynamic systems [x, P] = ukf(f,x,P,h,z,Q,R) returns state estimate, x and state covariance, P

说明：  谐波小波滤波再加上随机减量，谐波小波相当于带通滤波器，效果很好(Coupled random harmonic wavelet filter reduction, harmonic wavelet is equivalent to band-pass filter, the effect is very good)

计算矩阵各点之间的距离，并选取合适的点，适用于大型矩阵的计算(Calculate the distance between the points of the matrix)

离散论域模糊控制表的离线计算.已知单变量两维输入，一维输出模糊控制器，其两维输入为E，EC，一维输出为I，论域均为E，EC，I∈{-6，-5，-4，-3，-2，-1，0，1，2，3，4，5，6}，E的论域划分为{NB，NM，NS，NZ，PZ，PS，PM，PB}，共8个模糊子集(Discrete domain of fuzzy control table offline computing. Known univariate two-dimensional input, one-dimensional output fuzzy controller, the two-dimensional input of E, EC, one-dimensional output as I, on the field are E, EC, I ∈ {-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6}, E of the domain is divided into {NB, NM, NS, NZ, PZ , PS, PM, PB}, a total of eight fuzzy subset)

The PSNR is most commonly used as a measure of quality of reconstruction of lossy compression codecs (e.g., for image compression). The signal in this case is the original data, and the noise is the error introduced by compression

认知无线电 频谱检测技术 基于MATLAB的仿真(cognitive radio)

FDTD homeworks for waveguides and microstrip 2D

function [clusters,c,F]=fisher_classify(A,B,data) fisher判别法程序 输入A、B为已知类别样本的样本-变量矩阵，data为待分类样本 输出C为判别系数向量 (function [clusters, c, F] = fisher_classify (A, B, data) fisher discriminant method procedures input A, B for a sample of known types of samples- variable matrix, data to be classified as output samples for discriminant coefficient vector C)