How to train the neural network?

I tried using the help function. and I tried something like this

EnergyDemandDatasetTarget = dataset('XLSFile','CaseStudyCol.xlsx');
load EnergyDemandDatasetCol;
T = EnergyDemandDatasetCol; 
net = narnet(1:2,10);
[Xs,Xi,Ai,Ts] = preparets(net,{},{},T);
net = train(net,X,T,Xi,Ai);
view(net)
Y = net(Xs,Xi,Ai);
plotresponse(T,Y)

But I keep getting error for T = EnergyDemandDatasetCol;

Sorry because I am a slow learner and thank you for helping.

Hi Greg. Thanks for the help all these while. I tried running my data on narxnet as I am suppose to predict data then I modified it by a little.

I have an article for ANN for transport energy demand modelling.

I have Energy Demand as the target and 3 input, population, veh-km and GNP. However I am unable to create a multiple input, or rather I do not know how to. I seek you assistance in this.

From these values, I am suppose to train the network and predict the demand for the next 5 years.

% 1. Importing data
Data_Inputs=xlsread('C:UsersuserDesktopFYPCaseStudyCaseStudyCol.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');

Did I define the inputs and targets correctly? How do you show the graph that presents the prediction data? Where does the regression graph appear after the network is being trained? And how do I know if it is a good result?