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			errorfunction=>'ERROR TRANSFORMATION FUNCTION',
			randomweights=>MAX VALUE OF STARTING WEIGHTS);


The activation function must be defined in AI::NNFlex::Mathlib. Valid predefined activation functions are tanh & linear.

The error transformation function defines a transform that is done on the error value. It must be a valid function in AI::NNFlex::Mathlib. Using a non linear transformation function on the error value can sometimes speed up training.

The following parameters are optional:

 persistentactivation

 decay

 randomactivation

 threshold

 errorfunction

 randomweights



=head2 init

 Syntax:

 $network->init();

Initialises connections between nodes, sets initial weights and loads external components. Implements connections backwards and forwards from each node in each layer to each node in the preceeding and following layers, and initialises weights values ...

=head2 lesion

 $network->lesion ({'nodes'=>PROBABILITY,'connections'=>PROBABILITY})

 Damages the network.

B<PROBABILITY>

A value between 0 and 1, denoting the probability of a given node or connection being damaged.

Note: this method may be called on a per network, per node or per layer basis using the appropriate object.

=head2 AN::NNFlex::Dataset

=head2 learn

 $dataset->learn($network)

'Teaches' the network the dataset using the networks defined learning algorithm. Returns sqrError;

=head2 run

 $dataset->run($network)

Runs the dataset through the network and returns a reference to an array of output patterns.

=head1 EXAMPLES

See the code in ./examples. For any given version of NNFlex, xor.pl will contain the latest functionality.


=head1 PREREQs

None. NNFlex::Backprop should run OK on any version of Perl 5 >. 


=head1 ACKNOWLEDGEMENTS

Phil Brierley, for his excellent free java code, that solved my backprop problem

Dr Martin Le Voi, for help with concepts of NN in the early stages

Dr David Plaut, for help with the project that this code was originally intended for.

Graciliano M.Passos for suggestions & improved code (see SEE ALSO).

Dr Scott Fahlman, whose very readable paper 'An empirical study of learning speed in backpropagation networks' (1988) has driven many of the improvements made so far.

=head1 SEE ALSO

 AI::NNFlex

 AI::NNEasy - Developed by Graciliano M.Passos 
 Shares some common code with NNFlex. 
 

=head1 TODO



=head1 CHANGES


=head1 COPYRIGHT

Copyright (c) 2004-2005 Charles Colbourn. All rights reserved. This program is free software; you can redistribute it and/or modify it under the same terms as Perl itself.

=head1 CONTACT

 charlesc@nnflex.g0n.net



=cut