-
dq_to_abc
for simulink clarke transform
- 2010-07-14 17:06:26下载
- 积分:1
-
cubo
this file contains some m-files about calculating the fractional fourier transform
- 2010-10-24 18:16:25下载
- 积分:1
-
BPSK_BER_GAIN_OFDM
BPSK_BER_GAIN_OFDM
Phase Shift Keying
- 2015-04-02 01:07:32下载
- 积分:1
-
A-Guide-to-MATLAB-for-Beginners
说明: 一本matlab初学者的入门级经典书籍编程人员案头必备的一本参考书籍,英文版,有助于提高英文及编程技术.(An entry-level matlab beginner programmers classic books necessary for a desk reference book, in English, help to improve English and programming.)
- 2011-02-19 11:18:14下载
- 积分:1
-
Lossy_compression_DCT
it is matlab code for compress the image
- 2014-12-10 12:45:03下载
- 积分:1
-
FastFloyd
floyd warshall algorithm
- 2010-12-05 23:54:15下载
- 积分:1
-
PDCCH
PDCCH的链路仿真,需要的朋友可以参考一下。(PDCCH link simulation, the needs of friends can reference.)
- 2009-12-28 20:35:09下载
- 积分:1
-
mfiles
matlab6.5的源代码,包含了所有实例的源代码,请多多支持!!(matlab6.5 source code, contains all instances of the source code, a lot of support! !)
- 2011-06-21 20:57:49下载
- 积分:1
-
Collocation
this code for least square collocation estimation
- 2010-10-26 11:28:56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