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KRON

于 2015-10-08 发布文件大小:1KB

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A compressed sensing algorithm with the kronecker product

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KRON.m,3021,2015-10-07

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DSPanditsmatlab(2004)
《数字信号处理及其MATLAB实现》程序集(2004)( Digital signal processing and MATLAB to achieve the assembly (2004))

2008-07-25 11:52:21下载

积分：1
qam
一、本程序采用16QAM调制方式，对一串2进制信源进行调制，用升余弦滚降函数进行基带调制，再调到高频信道；在信道上加入高斯白噪声，运用匹配滤波器解调，画出解调星座图，运用最小欧氏距离译码判决，计算误比特率。(First, the program uses 16QAM modulation, the source of a string of two binary modulation, with the Raised Cosine modulated baseband functions, and then transferred to high-frequency channel in the channel by adding Gaussian white noise, the use of matched filter demodulation, demodulation constellation plot using the minimum Euclidean distance decoding decision, bit error rate calculation.)

2011-04-20 22:24:52下载

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SeazonDifProcess
用matlab编写季节性差分处理程序！！！！！！！！！1(using Matlab preparation procedures for handling seasonal difference! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! 1)

2007-03-25 17:54:45下载

积分：1
generator_color_noise
This program generates a color signal as the input signal and a desired signal of the idetification plant

2007-12-16 22:57:19下载

积分：1
RBFnn
rbfnn算法用于长期预测，效果较好。rbfnn算法用于长期预测，效果较好。(rbfnn algorithm for long-term projections, the effect is better. rbfnn algorithm for long-term projections, the effect is better.)

2010-12-14 11:10:56下载

积分：1
K_distribution
海杂波K-分布模型模拟仿真程序海杂波K-分布模型模拟仿真程序(K distribution in matlab )

2012-06-12 12:52:26下载

积分：1
transformer
simple ac transformer circuit

2012-07-14 08:45:24下载

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lenging_v43
基于分段非线性权重值的Pso算法，实现典型相关分析，是机器学习的例程。( Based on piecewise nonlinear weight value Pso algorithm, Achieve canonical correlation analysis, Machine learning routines.)

2016-08-29 14:44:25下载

积分：1
test_1
Clark变换与Inv_Clark变换，引入输入信号进过测试能够实现功能(Clark and Inv_Clark)

2013-09-05 16:45:10下载

积分：1
StepclassV2
逐步判别分析的主函数用于分类 [sel,c,c0,re,P]=StepclaassV2(data,[50 50 50],data,10) 输入变量 x为训练集.每行为一个样本，每列为一个变量. Class_x,为训练集的分类情况，一行，为各类样本数量,例如[5 6 9] Test为待分类样本. 输出变量：sel为选择的变量序号，c,c0为拟合出的判别函数.re为对Test的判别结果,P为其后验概率. author 王新 2012.4.8 例子Sample: load fisheriris x=meas Class_x=[50,50,50] Test=meas(1:5,:) Fa1=4 [sel,c,c0,re,P]=StepclaassV2(x,Class_x,Test,Fa1)( Stepwise discriminant analysis for the classification of the main function [sel, c, c0, re, P] = StepclaassV2 (data, [50 50 50], data, 10) input variables x as the training set, each conduct a sample of each as a variable. Class_x, the classification for the training set, a row, the number of samples for all types, for example, [5 6 9] Test sample to be classified. output variable: sel for the selected variables serial number, c, c0 is fitted discriminant function. re discrimination results for the Test, P its posterior probability. author Wang 2012.4.8 examples Sample: load fisheriris x = meas Class_x = [50,50,50] Test = meas (1:5, :) Fa1 = 4 [sel, c, c0, re, P] = StepclaassV2 (x, Class_x, Test, Fa1))

2020-09-18 12:27:56下载

积分：1

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