我有一篇关于ANN用于运输能源需求建模的文章。
我有能源需求作为目标和3输入，人口，车辆公里和GNP。但是我无法创建多个输入，或者我不知道如何。我在这寻求你的帮助。
从这些价值观来看，我想培训网络并预测未来5年的需求。
% 1. Importing data
Data_Inputs=xlsread('C:\Users\user\Desktop\FYP\CaseStudy\CaseStudyCol.xlsx'); % Import file
Training_Set1=Data_Inputs(1:end,3);%specific training set
Target_Set=Data_Inputs(1:end,2); %specific target set
Input=Training_Set1'; %Convert to row
Target=Target_Set'; %Convert to row
X = con2seq(Input); %Convert to cell
T = con2seq(Target); %Convert to cell
tr = Data_Inputs(1:end,1);
% 2. Data preparation
N = 5; % Multi-step ahead prediction
% Input and target series are divided in two groups of data:
% 1st group: used to train the network
inputSeries = X(1:end-N);
targetSeries = T(1:end-N);
inputSeriesVal = X(end-N+1:end);
targetSeriesVal = T(end-N+1:end);
% Create a Nonlinear Autoregressive Network with External Input
delay = 2;
inputDelays = 1:2;
feedbackDelays = 1:2;
hiddenLayerSize = 2;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
% Prepare the Data for Training and Simulation
% The function PREPARETS prepares timeseries data for a particular network,
% shifting time by the minimum amount to fill input states and layer states.
% Using PREPARETS allows you to keep your original time series data unchanged, while
% easily customizing it for networks with differing numbers of delays, with
% open loop or closed loop feedback modes.
%[Xs,Xi,Ai,Ts] = preparets(net,inputSeries,{},targetSeries);
[inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
% Train the Network
net = 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)
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotregression(targets,outputs)
%figure, plotresponse(targets,outputs)
%figure, ploterrcorr(errors)
%figure, plotinerrcorr(inputs,errors)
% Closed Loop Network
% Use this network to do multi-step prediction.
% The function CLOSELOOP replaces the feedback input with a direct
% 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
% 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);
% 5. Multi-step ahead prediction
inputSeriesPred = [inputSeries(end-delay+1:end),inputSeriesVal];
targetSeriesPred = [targetSeries(end-delay+1:end), con2seq(nan(1,N))];
[Xs,Xi,Ai,Ts] = preparets(netc,inputSeriesPred,{},targetSeriesPred);
yPred = netc(Xs,Xi,Ai);
perf = perform(net,yPred,targetSeriesVal);
figure;
plot([cell2mat(targetSeries),nan(1,N);
nan(1,length(targetSeries)),cell2mat(yPred);
nan(1,length(targetSeries)),cell2mat(targetSeriesVal)]')
legend('Original Targets','Network Predictions','Expected Outputs');
我尝试使用神经网络工具箱，nnstart并尝试使用应用程序，时间序列工具。我尝试加载可用的数据集。我是否正确定义了输入和目标？如何显示呈现预测数据的图表？在训练网络后，回归图出现在哪里？我怎么知道它是否是一个好结果？