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Simulation
光纤耦合器中光孤子传输仿真研究__本文用matlab对光孤子在光纤耦合器中传输的过程进行仿真。(Optical fiber coupler in the simulation of optical soliton transmission using matlab __ optical solitons in optical fiber coupler in the transmission process simulation.)
- 2009-04-03 21:03:45下载
- 积分:1
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main
antenna design using matlab
- 2011-05-07 01:06:15下载
- 积分:1
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2013A
2013年美国大学生数学建模竞赛A题程序(the code of 2013 mcm(A))
- 2013-05-19 22:09:42下载
- 积分:1
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annealing
说明: 用MATLAB编写的模拟退后程序,为通用模板,下载后修改即可使用。(Using MATLAB simulation back to prepare procedures for the generic template, modified to use after downloading.)
- 2008-11-09 17:21:13下载
- 积分:1
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rectwindowfilter
lowpass filter using rectangular window
- 2013-03-05 00:15:03下载
- 积分:1
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ch06-UART
基于KL25的开发例程,本例程关于异步通信,附带相关底层构建,十分适合初学者参考编程。(Based on KL25 development routine, the routine of asynchronous communication, with related underlying build, programming is very suitable for beginners reference.)
- 2013-12-17 21:58:51下载
- 积分:1
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Mackay构造LDPC码程序 matlab
说明: Mackay构造LDPC码程序 matlab(Mackay Constructs LDPC Code Program Matlab)
- 2019-04-09 16:23:15下载
- 积分:1
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timing_syn
说明: 采用gardner法,实现定时定时同步,做这个已经有一段时间了(By gardner method, to achieve timing synchronization time, to do this has been for some time)
- 2011-03-24 12:50:37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
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Tu_hoc_Matlab
tai lieu huong dan su dung matlab
- 2012-05-19 21:09:43下载
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