各位前辈~~ 我以ntstool产生一个narx的时间序列预测 但因每次执行，所产生的结果都不一样 也不知道哪一个结果比较好 不知道大家在做类神经网路的时候都怎麽修改这样的问题 我的想法是 先做一次执行，若预测值与目标值误差太大，则继续训练 但不知道在narx这样的时间序列预测要怎麽修改?? 可否教我怎麽改 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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