Please help with narnext error Subscripted assignment dimension mismatch.????

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SONGBIAN ZIME 2014-5-22

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https://ww2.mathworks.cn/matlabcentral/answers/130579-please-help-with-narnext-error-subscripted-assignment-dimension-mismatch

回答： Greg Heath 2019-7-25

code for GDPA prediction.m

I need really help, 2 months ago I can't fix the error,My goal is to predict the GDPA for next 5 years using narxnet on matlab. The data is time serie 23 rows ( 1990-2012), the input 17 attributes , 17 colums (1 to 17), the output is the last colum, the GDPA (18 th) I have the following error. ??? Subscripted assignment dimension mismatch.

Error in ==> preparets at 230 xx(inputFeedbackInd,TSind) = openFeedback; please here my code. and screenshot of Data.

    % %%Transform Raw Data into Time Series for the Model
data_inputs=xlsread('GDPA.xlsx');
% input the data; 
x1=data_inputs(1,1:17);
x2=data_inputs(2,1:17);
x3=data_inputs(3,1:17);
x4=data_inputs(4,1:17);
x5=data_inputs(5,1:17);
x6=data_inputs(6,1:17);
x7=data_inputs(6,1:17);
x8=data_inputs(7,1:17);
x9=data_inputs(9,1:17);
x10=data_inputs(10,1:17);
x11=data_inputs(11,1:17);
x12=data_inputs(12,1:17);
x13=data_inputs(13,1:17);
x14=data_inputs(14,1:17);
x15=data_inputs(15,1:17);
x16=data_inputs(16,1:17);
x17=data_inputs(17,1:17);
x18=data_inputs(18,1:17);
x19=data_inputs(19,1:17);
x20=data_inputs(20,1:17);
x21=data_inputs(21,1:17);
x22=data_inputs(22,1:17);
x23=data_inputs(23,1:17);
y=data_inputs(:,18);
%Transform into Neural Network Data
X1 = num2cell(x1);
X2 = num2cell(x2);
X3 = num2cell(x3);
X4 = num2cell(x4);
X5 = num2cell(x5);
X6 = num2cell(x6);
X7 = num2cell(x7);
X8 = num2cell(x8);
X9 = num2cell(x9);
X10 = num2cell(x10);
X11 = num2cell(x11);
X12 = num2cell(x12);
X13 = num2cell(x13);
X14 = num2cell(x14);
X15 = num2cell(x15);
X16 = num2cell(x16);
X17 = num2cell(x17);
X18 = num2cell(x18);
X19 = num2cell(x19);
X20 = num2cell(x20);
X21 = num2cell(x21);
X22 = num2cell(x22);
X23 = num2cell(x23);
yt = num2cell(y)';
%%Inputs and target
Input = catelements(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16,X17,X18,X19,X20,X21,X22,X23);
Target=yt;
%%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  = Input(:,1:end-N);
%inputSeries  = Input(1:end-N);
targetSeries = Target(1:end-N);
%second group this is the new data used for simulation. inputSeriesVal will be used for predicting new targets. targetSeriesVal will be used for network validation after prediction
inputSeriesVal  = Input(:,end-N+1:end); 
targetSeriesVal = Target(end-N+1:end); % This is generally not available
inputDelays = 1:3;
feedbackDelays = 1:3;
hiddenLayerSize = 50;
%%3-Network Creation
%Create a Nonlinear Autoregressive Network with External Input
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
delay = 4;
neuronsHiddenLayer = 25;
net = narxnet(1:delay,1:delay,neuronsHiddenLayer);
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'}; 
%%4 Training the network
net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'};
% 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(1,:),{},targetSeries);
% Setup Division of Data for Training, Validation, Testing
% The function DIVIDERAND randomly assigns target values to training,
% validation and test sets during training.
% For a list of all data division functions type: help nndivide
net.divideFcn = 'dividerand';  % Divide data randomly
% The property DIVIDEMODE set to TIMESTEP means that targets are divided
% into training, validation and test sets according to timesteps.
% For a list of data division modes type: help nntype_data_division_mode
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
% For a list of all training functions type: help nntrain
% Customize training parameters at: net.trainParam
net.trainFcn = 'trainlm';  % Levenberg-Marquardt
% Choose a Performance Function
% For a list of all performance functions type: help nnperformance
% Customize performance parameters at: net.performParam
net.performFcn = 'mse';  % Mean squared error
% Choose Plot Functions
% For a list of all plot functions type: help nnplot
% 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 for the series-parallel implementation, only
% one-step-ahead prediction
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)
Y = net(inputs,inputStates,layerStates);
%%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(1,end-delay+1:end),inputSeriesVal(1,:)]; 
targetSeriesPred = [targetSeries(end-delay+1:end), con2seq(nan(1,N))];
netc = closeloop(net); 
view(netc)
%%6-Neural Network Prediction Compared against Actual Market Price
[inputs,inputStates,layerStates,targets] = preparets(netc,inputSeriesPred,{},targetSeriesPred);
yPred = netc(inputs,inputStates,layerStates);
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')