Train nonlinear input-output neural network with multiple data sets

I have 5 sets of inputs corresponding outputs (all 1X100 data points) that I want to use to train a nonlinear input-output network. I'm trying to avoid combining inputs into one large set to avoid jumps in the data and because the network will be used with 1X100 size inputs. Is there a way to adjust the script that the GUI creates, or combine the data, to allow for a network to be trained multiple times with different data sets?

[Matheus Torquato]

I have the same question. I would like to train a Nonlinear Input-Output NN without using the Matlab ntstool.

Answer 1

[Greg Heath]

ALL OF YOUR FEARS ARE UNFOUNDED. IN PARTICULAR

1. Combining inputs into one large set to obtain jumps in the data can be beneficial

2. For regression and classification: As long as the training set is large and diverse enough to obtain relatively stable weight estimates and the test set is large enough to obtain relatively stable output estimates, the relative sizes of the training and operational data sets is irrelevant.

Hope this helps

 *Thank you for formally accepting my answer*

Greg