各位前輩~~ 我以ntstool產生一個narx的時間序列預測 但因每次執行，所產生的結果都不一樣 也不知道哪一個結果比較好 不知道大家在做類神經網路的時候都怎麼修改這樣的問題 我的想法是 先做一次執行，若預測值與目標值誤差太大，則繼續訓練 但不知道在narx這樣的時間序列預測要怎麼修改?? 可否教我怎麼改 clear all clc rawInputData=xlsread('BundDaily.xlsx','sheet1','B2:AG30'); rawTargetData=xlsread('BundDaily.xlsx','sheet2','E2:E30'); % rawTargetData - feedback time series. inputSeries = tonndata(rawInputData,false,false); targetSeries = tonndata(rawTargetData,false,false); % Create a Nonlinear Autoregressive Network with External Input inputDelays = 1:2; feedbackDelays = 1:2; hiddenLayerSize = 10; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize); [inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries); % Setup Division of Data for Training, Validation, Testing net.divideParam.trainRatio = 60/100; net.divideParam.valRatio = 20/100; net.divideParam.testRatio = 20/100; % Train the Network [net,tr] = train(net,inputs,targets,inputStates,layerStates); % Test the Network outputs = net(inputs,inputStates,layerStates); errors = gsubtract(targets,outputs); performance = perform(net,targets,outputs); % View the Network view(net) netc = closeloop(net); netc.name = [net.name ' - Closed Loop']; view(netc) [xc,xic,aic,tc] = preparets(netc,inputSeries,{},targetSeries); yc = netc(xc,xic,aic); closedLoopPerformance = perform(netc,tc,yc) %============================= % figure(1) % plot([cell2mat(yc)' cell2mat(tc)']) % Early Prediction Network % For some applications it helps to get the prediction a timestep early. % The original network returns predicted y(t+1) at the same time it is given y(t+1). % For some applications such as decision making, it would help to have predicted % y(t+1) once y(t) is available, but before the actual y(t+1) occurs. % The network can be made to return its output a timestep early by removing one delay % so that its minimal tap delay is now 0 instead of 1. The new network returns the % same outputs as the original network, but outputs are shifted left one timestep. nets = removedelay(net); nets.name = [net.name ' - Predict One Step Ahead']; view(nets) [xs,xis,ais,ts] = preparets(nets,inputSeries,{},targetSeries); ys = nets(xs,xis,ais); earlyPredictPerformance = perform(nets,ts,ys) figure(1) plot([cell2mat(ys)' cell2mat(ts)']) --

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