MATLAB: Error ??? Index exceeds matrix dimensions.

error index exceeds matrix dimensions

%
--------------------------------------------------------------------------
%define input and output for training
in=d(1:40,1:1000); 
ou=d(41:40,1:1000); 
trainInput= d(1:40,201:1000); % input for training
trainOutput= d(41:40,201:1000); % output for training
%--------------------------------------------------
% training the MLP Neural network 
% newff(input, target,[hidden layer1 hiddenlayer2 output],{transferFunction1 transferFunction2 transferFunction3});
% transferFunction defult for hidden layer is tansig but here use logsig
% and purelin is default transferFunction for output layer
net = newff(minmax(in),ou,[10 10 4],{'logsig' 'logsig' 'purelin'});           
%--------------------------------------------------------------------------

% Setting the parameters for training
net.trainParam.epochs = 1000; %set the maximum number of epochs to train
net.trainParam.goal = 0.02;  %sum-squared error goal.
%-------------------------------------------------------------------------- 
% training the MLP by using a a train function
net = train(net,trainInput,trainOutput); 
%--------------------------------------------------------------------------
% To draw the result making the testing 
testInput=d(1:40,1:200);   % extract certain column for testing input
testResult1=sim(net,testInput); %  simulate  the  MLP  network  by  using  a    sim function
testResult1 = testResult1';
trainOutput2= d(1:40,1:200);
??? Index exceeds matrix dimensions.
above is my code for fold 1 (train set is 201-1000, test set is 1-200) i use dataset 1000×40 double Any expert can u help me…thank you!!!

Best Answer

Did it ever occur to you that it is self defeating to include code with a data set that is not accessible to those who want to help you?
In the future try your code on a MATLAB data set before posting. That way you can separate code errors from data errors and get a headstart on receiving focused help.
 help nndatasets
 doc nndatasets
Either use all of the chosen MATLAB dataset or a subset of it to best match the size of your data.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%define input and output for training
 in=d(1:40,1:1000); 
 ou=d(41:40,1:1000);
'1. ERRONEOUS INDEXING'
 trainInput= d(1:40,201:1000); % input for training
 trainOutput= d(41:40,201:1000); % output for training
'2.ERRONEOUS INDEXING'
%--------------------------------------------------
% training the MLP Neural network
 % newff(input, target,[hidden layer1 hiddenlayer2 output], ...
 % {transferFunction1 transferFunction2 transferFunction3});
 % transferFunction defult for hidden layer is tansig but here use logsig
 % and purelin is default transferFunction for output layer
 net = newff(minmax(in),ou,[10 10 4],{'logsig' 'logsig' 'purelin'});
'3. DOUBLY OBSOLETE FUNCTION. USE FITNET.'
'4. ONE HIDDEN LAYER IS SUFFICIENT'
'5. BIPOLAR TANSIG IS BETTER FOR HIDDEN LAYERS THAN UNIPOLAR LOGSIG'
'6. 4 OUTPUT NODES IS ERRONEOUS'
%--------------------------------------------------------------------------
 % Setting the parameters for training
 net.trainParam.epochs = 1000; %set the maximum number of epochs to train
'7. epochs = 1000 IS A DEFAULT. WHY BOTHER?'
 net.trainParam.goal = 0.02;  %sum-squared error goal.
'8. WHAT IS THE RATIONALE FOR THIS CHOICE??...(I ASSUME YOU MEANT MSE INSTEAD OF SSE)'
 %-------------------------------------------------------------------------- 
 % training the MLP by using a a train function
 net = train(net,trainInput,trainOutput); 
 %--------------------------------------------------------------------------
 % To draw the result making the testing 
 testInput=d(1:40,1:200);   % extract certain column for testing input
 testResult1=sim(net,testInput); %  simulate  the  MLP  network  by  using  a    sim function
 testResult1 = testResult1';
 trainOutput2= d(1:40,1:200);
'9.ERRONEOUS INDEXING'
THANK YOU FOR FORMALLY ACCEPTING MY ANSWER
GREG
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