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nono2009
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No gains, no pains.
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ר¼Ò¾Ñé: +21105 - Ó¦Öú: 28684 (Ժʿ)
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2Â¥2012-09-10 16:41:56
csgt0
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ר¼Ò¾Ñé: +2 - Ó¦Öú: 367 (˶ʿ)
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3Â¥2012-09-10 16:45:26
ben_moody
ľ³æ (ÕýʽдÊÖ)
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4Â¥2012-09-11 09:48:02
ben_moody
ľ³æ (ÕýʽдÊÖ)
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5Â¥2012-09-11 09:53:22
salor
ľ³æ (³õÈëÎÄ̳)
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inputs = P'; targets = T';hiddenLayerSize = 10; net = fitnet(hiddenLayerSize); net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; net.outputs{2}.processFcns = {'removeconstantrows','mapminmax'}; net.divideFcn = 'dividerand'; % Divide data randomly net.divideMode = 'sample'; % Divide up every sample net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100;net.trainFcn = 'trainlm'; % Levenberg-Marquardt net.performFcn = 'mse'; % Mean squared error net.plotFcns = {'plotperform','plottrainstate','ploterrhist', ... 'plotregression', 'plotfit'}; [net,tr] = train(net,inputs,targets); outputs = net(inputs);errors = gsubtract(targets,outputs); performance = perform(net,targets,outputs) trainTargets = targets .* tr.trainMask{1}; valTargets = targets .* tr.valMask{1}; testTargets = targets .* tr.testMask{1}; trainPerformance = perform(net,trainTargets,outputs) valPerformance = perform(net,valTargets,outputs) testPerformance = perform(net,testTargets,outputs) view(net) T_p=sim(net,P') |
6Â¥2013-01-14 18:10:36
ben_moody
ľ³æ (ÕýʽдÊÖ)
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7Â¥2013-01-16 22:48:47
³¬ÈË1048
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8Â¥2013-06-28 22:07:58
ÔÚˮһ·½110
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- ×¢²á: 2012-12-28
- ÐÔ±ð: MM
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9Â¥2014-03-19 11:44:00
ben_moody
ľ³æ (ÕýʽдÊÖ)
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- רҵ: ʳƷ¿ÆÑ§»ù´¡

10Â¥2014-03-19 15:51:41














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