MATLAB: How to test a large data set(excelsheet) on a neural network?and how to save a trained network to test it in future with new data

saving and testing ann

how a trained network can be used to test new inputs and oututs on which it was not trained

 load savednet
 newoutput = savednet(newinput);

Thank you for formally accepting my answer

Greg

 Use the command window
 help mse
 doc mse

Hope this helps.

Thank you for formally accepting my answer

Greg

I assume target T is 1-dimensional.

What is the size of the X matrix?

Are X(i,:) and T stationary (e.g., are the ten 63 point means and variances of each variable time-invariant?)

I recommend standardizing X and T with zscore or mapstd.

What are the significant crosscorrelation function lags between X(i,:) and T?

Are you including zero input lag? Unfortunately, it is not a default.

MSE results are more accurately assessed by % of target variance.

MSE00 = mean(var(T',1)) %MSE of NAIVE constant model y = mean(T,2)

NMSE = MSE/MSE00 % Normalized MSE

R^2 = 1-NMSE % Fraction of target variance modeled (Search coefficient of determination in Wikipedia)

Have you replaced the 'dividerand' (correlation destroying) default division option with 'divideblock' or another divide option?

Hope this helps.

Thank you for formally accepting my answer.

Greg