Could some one please explain me this MATLAB code for Neural Network

     Data_Inputs = xlsread('1.xlsx'); 
     Testing_Data = xlsread('2.xlsx');
    %The training data sample are randmonized by using the function'randperm'
   % Shuffling_Inputs=Data_Inputs(randperm(end),1:2); % integers (training sample) 
      Shuffling_Inputs = Data_Inputs(randperm(3649),1:4);
    Training_Set=Data_Inputs(1:3649,1:3);%specific training set
    Target_Set=Data_Inputs(1:3649,4); %specific target set
    Input=Training_Set'; %Convert to row
    Target=Target_Set'; %Convert to row
    X = con2seq(Input); %Convert to cell
    T = con2seq(Target); %Convert to cell
    %%2. Data preparation
    N = 365; % 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);

    inputSeries  = X(1:3649-N);
    targetSeries = T(1:3649-N);
    inputSeriesVal  = X(3649-N+1:3649);
    targetSeriesVal = T(3649-N+1:3649);

    % Create a Nonlinear Autoregressive Network with External Input
    delay = 4;
    inputDelays = 1:4;
    feedbackDelays = 1:4;
    hiddenLayerSize = 50;
    net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
    net.trainFcn = 'trainlm'; 
    net.trainParam.epochs=750;
    net.trainParam.max_fail = 10;
    net.trainparam.min_grad = 0.00000001; 
    net.trainParam.lr = 0.4;
    % 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.
    [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,tr] = train(net,inputs,targets,inputStates,layerStates);

    %net.trainParam.epochs = 1000; 

    % 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, plotwb(net)
    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');
    outputs = net(inputs);
trOut = outputs(tr.trainInd);
vOut = outputs(tr.valInd);
tsOut = outputs(tr.testInd);
trTarg = targets(tr.trainInd);
vTarg = targets(tr.valInd);
tsTarg = targets(tr.testInd);
plotregression(trTarg,trOut,'Train',vTarg,vOut,'Validation',...
tsTarg,tsOut,'Testing');

[José-Luis]

It would be easier if you had a more specific question? What is it you don't understand?

[Greg Heath]

Comment: WOW!