Sparse-Time-Frequency-Representat

采用模糊C均值对数据集data聚为cluster_n类(Fuzzy C mean data on the data sets together to cluster_n class)

数字信号处理2matlab误差性能分析 数字信号处理2matlab误差性能分析(error performance analyze surface)

数字基带传输系统的MATLAB仿真实现 function [sampl,re_sampl]=system_1(A,F,P,D,snr,m,N) 输入变量A ,F,P分别为输入信号的幅度、频率和相位，D为量化电平数，snr 为信道信噪比，N为D/A转换时的内插点数；输出变量sampl为抽样后的输入 信号，re_sampl为恢复出的输入信号。 数字基带传输系统的MATLAB仿真实现 [sampl,quant,pcm]=a_d_1(A,F,P,D) [changed_ami]=signal_encod_1(pcm) [ami_after_channel]=channel_1(changed_ami,snr) [adjudged_ami]=adjudg_1(ami_after_channel,m) re_pcm=signal_decod_1(adjudged_ami) [re_voltag,re_sampl,re_sampl1]=d_a_1(re_pcm,sampl,D,N)(Digital baseband transmission system implementation MATLAB simulation function [sampl, re_sampl] = system_1 (A, F, P, D, snr, m, N) input variables A, F, P, respectively, for the input signal amplitude, frequency and phase , D to quantify the number of electric-ping, snr for the channel signal to noise ratio, N to D/A converter when the interpolation points output variables for the sample after Sampl input signal, re_sampl for the restoration of the input signal. Digital baseband transmission system simulation MATLAB implementation [sampl, quant, pcm] = a_d_1 (A, F, P, D) [changed_ami] = signal_encod_1 (pcm) [ami_after_channel] = channel_1 (changed_ami, snr) [adjudged_ami] = adjudg_1 (ami_after_channel, m) re_pcm = signal_decod_1 (adjudged_ami) [re_voltag, re_sampl, re_sampl1] = d_a_1 (re_pcm, sampl, D, N))

仿真锁相环系统，可以仿真锁定时间。不同的环路带宽对系统的非理想特性！(PLL simulation system that can lock simulation time. Different loop bandwidth of the system of non-ideal characteristics!)

matlab读取dbf格式文件，使用成功可以。(matlab read dbf format file)

说明：  先进PID控制 MATLAB仿真 第2版 刘金琨等编.pdf (MATLAB simulation of advanced PID control 2nd Edition Hangzhou Press, eds. Pdf)

采用RLS算法实现自适应均衡，还有Simulink图，效果较好(Adaptive equalizer using RLS algorithm, as well as Simulink diagram, the effect is good)

（压缩包里一共有5个代码） pca+lda+粗糙集+模糊神经网络 saveORLimage.m将ORL人脸库分为测试集ptest和训练集pstudy存为imagedata.mat 1.savelda.m将人脸库先进行pca降维，再用lda进行特征提取，得到新的测试集ldatest和训练集ldastudy存为imageldadata.mat 2.对ldastudy进行离散化(discretimage.m)，得到离散化矩阵disdata,存入到imagedisdata.mat 3.将disdata组成决策表(savers.m)，通过对disdata的条件属性进行约简，得到其一个约简，组成新的测试集rstest和训练集rsstudy存为imagersdata.mat 4.对rsstudy进行模糊神经网络训练(savecul.m)，对模糊神经网络的参数进行调整学习将其存入culdata.mat 5.用runfnn.m对rstest进行测试得到其识别率 savem.m和cm.m是用最小距离分类器对训练集和测试集进行分类.(pca+ lda+ Rough Set+ fuzzy neural network saveORLimage.m will ORL face database is divided into test set and training set ptest for pstudy keep imagedata.mat Treasury will face 1.savelda.m first dimensionality reduction pca, lda used feature extraction, a new test set and training set ldatest for ldastudy keep imageldadata.mat 2. Ldastudy carried out on the discretization (discretimage.m), to be discrete matrix of disdata, deposited to imagedisdata.mat 3. Disdata the composition of the decision table (savers.m), the conditions on the attributes disdata about Jane, has been one of its reduction to form the new test set and training set rstest for rsstudy keep imagersdata.mat 4. Rsstudy training fuzzy neural network (savecul.m), on the parameters of fuzzy neural network to learn to adjust their deposit culdata.mat 5. Rstest used to test for runfnn.m by its recognition rate cm.m is savem.m and minimum distance classifier on the training set and test set classificati)

It can read file, read electric charge etc

matlab code for BPSK generation without using inbuilt functions and in AWGN channel