-
myLee
一个简单的Lee滤波,只是一般的设计方法,仅供参考!(A simple filter of lee, just for sharing!)
- 2010-07-14 11:45:05下载
- 积分:1
-
4Tx_2Rx_STBC
该文件夹为Alamouti空时码的仿真程序,天线配置为4发2收,与大家共享。(folder for the ACS code of space-time simulation program, antenna configuration for four 2 admission. and share.)
- 2021-03-18 20:39:20下载
- 积分:1
-
chapter6
仿真双极性信号通过AWGN信道后的误比特率性能(Simulation bipolar signal bit error rate performance with AWGN channel after。)
- 2015-06-24 21:36:09下载
- 积分:1
-
GSC
gsc 广义旁瓣消除器的MATLAB源程序(gsc GSC MATLAB source device)
- 2021-03-24 21:09:14下载
- 积分:1
-
modelchange
通过对颜色模型的转换来进行对图像进行处理与分析。(Color model of the conversion to image processing and analysis.)
- 2008-03-15 11:24:51下载
- 积分:1
-
EEG-analysis-and-application
《脑电信号分析方法及其应用》书中示例程序,书的附录中有该程序列表(EEG analysis method and its application(the book program))
- 2013-07-31 16:43:14下载
- 积分:1
-
MPPT
一种新的型光伏发电最大功率点跟踪(MPPT)控制方法(A new type of photovoltaic maximum power point tracking (MPPT) control method)
- 2020-10-04 17:17:40下载
- 积分:1
-
Newton
何仰赞版《电力系统分析》牛顿拉夫逊法潮流计算例题Matlab程序。(A matlab programme of an example in<Power system analysis>written by HeYangZan,which is based on the Newton s menthod.)
- 2014-12-06 12:33:12下载
- 积分:1
-
rf-energy-analysis-in-WSN
In wireless sensor network, residual energy of node is very important criteria. Life of wireless sensor network depend the energy of node. In most of the application, the nodes are battery operated. So it is very important to select the parameters like nodes deployment in network, size of packet, etc. In work,analyze node’s energy with respect to packet size, distance, time and also how much data can be transmitted. And finally we will conclude for the effective value of these parameters.
- 2014-01-08 01:36:45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 )
- 2011-05-21 16:47:44下载
- 积分:1