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CodageCanalLDPC
TP, documents et codes source pour les codes LDPC (Low Density Parity Check)
- 2010-12-02 09:52:15下载
- 积分：1
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nonlinearchar
this mdl shows the non linear characteristics of the fuzzy controller
- 2010-02-01 14:27:01下载
- 积分：1
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Selector_Usage
matlab/simmulink中Selector模块各个选项的详细示例。只是一个小小的示例，想详细了解Selector的话可以看看，会熟练应用的大神可以无视了。(The matlab/simmulink Selector module options detailed example. Just a small example, would like to learn more about the Selector, then you can take a look at.)
- 2012-08-27 17:28:13下载
- 积分：1
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kengyie_v53
小波包分析提取振动信号中的特征频率，有井曲线作为输入可计算其地震波的衰减,遗传算法无功优化。( Wavelet packet analysis to extract vibration signal characteristic frequency, There is a well attenuation curve as input to calculate its seismic waves, Genetic algorithm based reactive power optimization.)
- 2016-05-20 15:22:20下载
- 积分：1
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jianmoshuizaixunshi
matlab环境下的数学建模水灾巡视的模型及其代码(Mathematical modeling flood model and code inspections)
- 2011-02-13 21:18:36下载
- 积分：1
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BAM_Neural_Network
说明： BAM是一种两层的异联想，内容可寻址存储器组成的反馈网络。
分类：离散，连续 和自适应BAM等。
1987年和1988年由Kosko提出的BAM型。
网络运行是双向的。对网络的一端输入信号，则可在另一端得到输出，该输出又反馈回来，经过多次反复，直到网络达到稳态为止(BAM is a two-tier Heteroassociation, content addressable memory component of the feedback network. Classification : discrete, continuous and adaptive BAM other. In 1987 and 1988 by the BAM Kosko type. The operation of the network is a two-way. The network side input signal can be received at the other end output, the output feedback to come back again, repeatedly, until the network reached so far Steady)
- 2021-04-18 15:18:51下载
- 积分：1
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kftool_matlab
说明： kalman滤波的matlab程序，准确可靠，欢迎下载(Kalman filtering Matlab procedures, accurate and reliable, welcome to download)
- 2006-03-21 21:35:58下载
- 积分：1
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FullBNT-1.0.2
说明： 贝叶斯网络的matlab实现。可以创建贝叶斯网络、训练模型，以及实现统计推理(Bayesian network Matlab achieve. Bayesian networks can create, model training, and the realization of statistical reasoning)
- 2006-05-10 03:48:32下载
- 积分：1
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RBFFunction
RBF function for neural network
- 2012-10-02 03:07:01下载
- 积分：1
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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 )
- 2011-05-21 16:47:44下载
- 积分：1
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