Question

我听说神经网络工具箱是一个很好的工具箱，用于培训网络和预测。根据外部输入自回归问题（NARX）标准，我尝试在下面的代码示例中训练网络，并且它在训练中特别有效。我得到了simplenarx_dataset样本，为输入和目标制作了一个带有（1x100单元格）的样本数据集。

假设为2010年1月1日至2010年4月10日期间的100个数据集。我的问题是，我想预测2010年4月10日至2010年5月1日当天的结果。我该怎么做以及如何绘制它？非常感谢你的帮助！

以下是代码：

load simplenarx_dataset;
% SimplenarxInputs (1x100 cell)
% simplenarxTargets (1x100 cell)
% 100 cell as a data starting 1/1/2010 and ending 4/10/2010
% problem: predict data in the future, sample starting 5/10/2010 to 5/1/2010

inputSeries = simplenarxInputs;
targetSeries = simplenarxTargets;

% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:2;
feedbackDelays = 1:2;
hiddenLayerSize = 10;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);

% Customize output parameters at: net.outputs{i}.processParam
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'};

% open loop or closed loop feedback modes.
[inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);

% Setup Division of Data for Training, Validation, Testing
net.divideFcn = 'dividerand';  % Divide data randomly
net.divideMode = 'value';  % Divide up every value
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

% Choose a Training Function
net.trainFcn = 'trainlm';  % Levenberg-Marquardt
net.performFcn = 'mse';  % Mean squared error

% Customize plot parameters at: net.plotParam
net.plotFcns = {'plotperform','plottrainstate','plotresponse', ...
 'ploterrcorr', 'plotinerrcorr'};

% 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)

% Recalculate Training, Validation and Test Performance
trainTargets = gmultiply(targets,tr.trainMask);
valTargets = gmultiply(targets,tr.valMask);
testTargets = gmultiply(targets,tr.testMask);
trainPerformance = perform(net,trainTargets,outputs)
valPerformance = perform(net,valTargets,outputs)
testPerformance = perform(net,testTargets,outputs)

% View the Network
view(net)

% connection from the outout layer.
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)

% Early Prediction Network
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)

如何使用NARX训练网络后预测结果？

0 个答案: