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yhj1229½ð³æ (³õÈëÎÄ̳)
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´óÉñ¸ø½âÊÍÏÂÿһÐгÌÐòµÄº¬Ò壺 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'}; = 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') |
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