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feng4

于 2016-04-06 发布文件大小:1KB

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基于全通函数的线性相位参量均衡器的设计，一阶低通均衡器到二阶带通均衡器，对峰值均衡的实际值与理论值进行仿真(All pass function for linear phase parametric equalizer design based on, first order low pass equalizer to second-order passband equalizer, on peak equilibrium of the actual value and theoretical value simulation)

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feng4.m,1709,2016-03-29

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11
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说明：对图像进行TA置乱，请根据图像大小自行设定周期。(TA scrambling the image, the image size according to set their own cycle.)

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BP_decodenewc
本代码描述了LDPC码的BP译码算法的每一个步骤，详尽。(This code describes the decoding algorithm for LDPC codes BP each step, detailed.)

2011-05-23 12:28:47下载

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这是另一个matlab的贝叶斯分类器设计，可用作作业用(This is another matlab Bayesian classifier design can be used as operating with)

2011-06-07 22:56:04下载

积分：1
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杨晓菲信号与系统习题解答重邮用的黄色封面那本(signal and system)

2010-10-21 23:03:14下载

积分：1
shenjingwangluo
T=[1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] 输入向量的最大值和最小值 threshold=[0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net=newff(threshold,[31 3],{ tansig , logsig }, trainlm ) 训练次数为1000，训练目标为0.01，学习速率为0.1 net.trainParam.epochs=1000 net.trainParam.goal=0.01 LP.lr=0.1 net = train(net,P,T) 测试数据，和训练数据不一致 P_test=[0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 (T = [1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1] ' of the maximum and minimum input vector threshold = [0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1] net = newff (threshold, [31 3], {' tansig' , ' logsig' }, ' trainlm' ) training times for the 1000 target of 0.01 training, learning rate of 0.1 net.trainParam.epochs = 1000 net. trainParam.goal = 0.01 LP.lr = 0.1 net = train (net, P, T) test data, and training data inconsistencies P_test = [0.2101 0.0950 0.1298 0.1359 0.2601 0.1001 0.0753 0.0890 0.0389 0.1451 0.0128 0.1590 0.2452 0.0512 0.1319 0.2593 0.1800 0.0711 0.2801 0.1501 0.1298 0.1001 0.1891 0.2531 0.0875 0.0058 0.1803 0.0992 0.0802 0.1002 )

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delta_figure
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