dau_wyt asked . 2021-07-07

Artificial Neural Network Prediction Code in Matlab, please help !!!

    % Data_Inputs=xlsread('1.xls'); % Import file

    %The training data sample are randmonized by using the function'randperm'
    %Shuffling_Inputs=Data_Inputs(randperm(end),1:2); % integers (training sample) 

    Training_Set=Data_Inputs(1:end,1);%specific training set

    Target_Set=Data_Inputs(1:end,2); %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); 
    % Create a Nonlinear Autoregressive Network with External Input
    delay = 2;
    inputDelays = 1:2;
    feedbackDelays = 1:2;
    hiddenLayerSize = 100;
    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.
    [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);

    % 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');

neural network , prediction , matlab , programming