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bcs_ver0.1

于 2021-01-09 发布文件大小:1069KB

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下载积分: 1 下载次数: 5

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代码说明：

贝叶斯压缩感知算法完整代码。值得初学者拥有。(The complete code of Bayesian compressed sensing algorithm, which is be worthy of a beginner.)

文件列表：

bcs_ver0.1\BCS_demo\approx_results.mat, 60254 , 2014-02-25
bcs_ver0.1\BCS_demo\BCS_fast_rvm.m, 5463 , 2014-02-25
bcs_ver0.1\BCS_demo\Fig2.m, 2096 , 2014-02-25
bcs_ver0.1\BCS_demo\Fig4_ab.m, 1259 , 2014-02-25
bcs_ver0.1\BCS_demo\multi_approx_measures.m, 1696 , 2014-02-25
bcs_ver0.1\BCS_demo\multi_optimized_measures.m, 1763 , 2014-02-25
bcs_ver0.1\BCS_demo\multi_random_measures.m, 1514 , 2014-02-25
bcs_ver0.1\BCS_demo\optimized_results.mat, 60300 , 2014-02-25
bcs_ver0.1\BCS_demo\random_results.mat, 60084 , 2014-02-25
bcs_ver0.1\MT_CS_demo\Fig2.m, 2662 , 2014-02-25
bcs_ver0.1\MT_CS_demo\Fig3.m, 1899 , 2014-02-25
bcs_ver0.1\MT_CS_demo\mt_CS.m, 6377 , 2014-02-25
bcs_ver0.1\MT_CS_demo\multi_results_25.mat, 299828 , 2014-02-25
bcs_ver0.1\MT_CS_demo\multi_results_50.mat, 299942 , 2014-02-25
bcs_ver0.1\MT_CS_demo\multi_results_75.mat, 300077 , 2014-02-25
bcs_ver0.1\MT_CS_demo\multi_runs_25.m, 2569 , 2014-02-25
bcs_ver0.1\MT_CS_demo\multi_runs_50.m, 2602 , 2014-02-25
bcs_ver0.1\MT_CS_demo\multi_runs_75.m, 2602 , 2014-02-25
bcs_ver0.1\README.txt, 2664 , 2014-02-25
bcs_ver0.1\BCS_demo, 0 , 2014-02-25
bcs_ver0.1\MT_CS_demo, 0 , 2014-02-25
bcs_ver0.1, 0 , 2014-02-25

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we
说明：七单元天线阵DOA估计 clear clc d=1 天线阵元的间距 lma=2 信号中心波长 q1=1*pi/4 q2=1*pi/3 q3=1*pi/6 q4=3*pi/4 四输入信号的方向 A1=[exp(-2*pi*j*d*[0:6]*cos(q1)/lma)] 求阵因子 A2=[exp(-2*pi*j*d*[0:6]*cos(q2)/lma)] A3=[exp(-2*pi*j*d*[0:6]*cos(q3)/lma)] A4=[exp(-2*pi*j*d*[0:6]*cos(q4)/lma)] A=[A1,A2,A3,A4] 得出A矩阵 n=1:1900 v1=.015 四信号的频率 v2=.05 v3=.02 v4=.035 d=[1.3*cos(v1*n) 1*sin(v2(we)

2010-05-06 15:26:50下载

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2007-06-21 09:45:43下载

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2009-10-14 09:55:37下载

积分：1
SGAPublic
说明： SGA（Simple Genetic Algorithm）是一种强大的智能多变量优化算法，它模仿种群繁殖规律来进行优化。本SGA可以优化变量，求最小值,最大值（当把函数倒数也就求最小值）并且支持浮点编码，grey编码，二进制编码；轮赌法选择，锦标赛选择；单点交叉，均布交叉，浮点交叉；单点变异，浮点变异；调用时Genetic（目标函数名）使用SGA时，首先需要一个目标函数（像AimFunc.m），该函数返回适应度输入变量为待优化变量x输出为一个适应度。然后修改Genetic.m中可以修改的地方(SGA (Simple Genetic Algorithm) is a powerful smart multi-variable optimization algorithm, which mimics the reproduction of law to be optimized. The SGA can be optimized variables, minimum, maximum, (when the function will last for the minimum) and supports the floating-point encoding, grey code, binary code round of betting method selection, tournament selection single-point crossover, uniform crossover , floating-point crossover single point mutation, floating-point mutation called Genetic (objective function name) the use of SGA, first of all need an objective function (like AimFunc.m), the fitness function returns the input variables to be optimized for the output variable x a fitness. Can be modified and then modify the place Genetic.m)

2011-03-15 15:33:34下载

积分：1

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