Back Propogation Algorithm

The code implements the Back prop algorithm for MLPs.

The training input vectors and target vectors are read from files data1in and data1out respectively. The no of nodes in input and output layer is decided depending on the no. of rows in these datasets.
The no of hidden layers, No of nodes in each hidden layer and the target error (put 0.1) is to be input by the user.

Learning curve is plotted after every 100 epochs.
Learning factor can be varied using the slider at the bottom. This idea was picked from an algorithm created by by AliReza KashaniPour & Phil Brierley.
Activation function for hidden layers is logsig and linear for output layer!
Just press F5 and ve funn!
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