Typical backpropagation algorithm is used for training.
In this file you can configure its parameters.

*********************
* Training patterns *
*********************

If you are going to train your network, you MUST first
define all training patterns. The easiest way is to describe
them is to do it in this file.

Give as much training patterns as necessary, using format:
"pattern: inputs -> desired outputs". Note that number of
inputs and outputs in patterns must match your network
structure given in configuration file.

  pattern: 0 0 -> 0
  pattern: 0 1 -> 1
  pattern: 1 0 -> 1
  pattern: 1 1 -> 0
                       

*******************************
* Training related parameters *
*******************************

Process of training depends on several parameters. If you
have no idea what they should be, then you may set them
to default values (which are given in help texts before
each parameter). Or just leave them empty (delete the
numbers after each parameter identifier), then they will be
automatically set to default values. Actually it is even allowed
to delete this section "Training related parameters" completely
from this file and leave only the "Training patterns" section.
Then everything will be set to default as well. But make a
backup of this file first ;)


- Parameter: error_limit. Type: double. Default value: 0.1 - -
Error limit is used for evaluating the success of training.
If all outputs are in a range from (desired_output - error_limit)
to (desired_output + error_limit), then training is completed.

  error_limit: 0.1

- Parameter: max_iterations. Type: int. Default value: 20000 - -
When this number of iterations is reached, training will be
stopped and is failed (i.e. outputs were still outside the
given error limits).

  max_iterations: 20000

- Parameter: learning_rate. Type: double. Default value: 5 - -
How fast should network learn? Too small values make
learning too slow. Too big may halt the learning process.

  learning_rate: 5

- Parameter: max_weight. Type: double. Default value: 10 - -
Maximal allowed weight for connections. Weights will be
in range -max_weight..+max_weight.

  max_weight: 10