AI-NeuralNet-Mesh

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Changes  view on Meta::CPAN

Revision history for Perl extension AI::NeuralNet::Mesh.

0.20  Tue Aug 22 18:01:04 2000
        - Original version; created by Josiah Bryan
        - Complete core re-write of AI::NeuralNet::BackProp
        - 23% More accuracy in learning
        - Increased learning speed
        - Better connection topology

0.31  Fri Aug 25 05:10:11 20000
        - Second release, by Josiah Bryan
        - 3 Major features:
                - seperate layer sizes
                - custom node activations
                - increased learning speed

0.43  Wed Sep 14 03:13:01 20000
        - Third release, by Josiah Bryan

Mesh.pm  view on Meta::CPAN

designers are working on weather forecasts by neural networks (Myself
included).  Currently, doctors are developing medical neural networks as an
aid in diagnosis.  Attorneys and insurance companies are also working on
neural networks to help estimate the value of claims.

Neural networks are poor at precise calculations and serial processing. They
are also unable to predict or recognize anything that does not inherently
contain some sort of pattern.  For example, they cannot predict the lottery,
since this is a random process.  It is unlikely that a neural network could
be built which has the capacity to think as well as a person does for two
reasons.  Neural networks are terrible at deduction, or logical thinking and
the human brain is just too complex to completely simulate.  Also, some
problems are too difficult for present technology.  Real vision, for
example, is a long way off.

In short, Neural Networks are poor at precise calculations, but good at
association, evaluation, and pattern recognition.


=head1 EXAMPLES

Included are several example files in the "examples" directory from the
distribution ZIP file. Each of the examples includes a short explanation 

examples/ex_aln.pl  view on Meta::CPAN

	File:	examples/ex_aln.pl
	Author:	Josiah Bryan, jdb@wcoil.com
	Desc:
	
	This is a simple example of a _basic_ ALN implementation in
	under 210 lines of code. In this demo we make use of the 
	custom node connector as described in the POD. We also 
	insert our own method over the node's internal adjust_weight()
	method to make ALN learning a bit easire. This demo also adds
	a temporary method to the network to print the logical type of 
	each node, called print_aln();

	print_aln() prints simple diagram of the
	network similar to this (this is for a $net=Tree(8,1) with 
	$net->learn([1,1,0,1,0,1,1,1],[0]), and each line represents 
	a layer):
	
	L R L L L L L L
	OR OR OR OR
	OR OR

examples/ex_aln.pl  view on Meta::CPAN

	# Use our nifty dot verbosity.
	$net->v(12);
	
	# Learn a pattern and print stats.
	if(!$net->load('aln.mesh')) {
		print "Learning";
		print "Done!\nLearning took ",$net->learn([1,1,0,1,0,1,1,1],[0]),"\n";
		$net->save('aln.mesh');
	}
		
	# Print logic gate types
	$net->print_aln();
	
	# Test it out
	print "\nPattern: [1,1,0,1,0,1,1,1]".
		  "\nResult: ",$net->run([1,1,1,1,1,1,1,1])->[0],"\n";




######################################################################

mesh.htm  view on Meta::CPAN

loan analysis and financial forecasting make good applications.  New network
designers are working on weather forecasts by neural networks (Myself
included).  Currently, doctors are developing medical neural networks as an
aid in diagnosis.  Attorneys and insurance companies are also working on
neural networks to help estimate the value of claims.</P>
<P>Neural networks are poor at precise calculations and serial processing. They
are also unable to predict or recognize anything that does not inherently
contain some sort of pattern.  For example, they cannot predict the lottery,
since this is a random process.  It is unlikely that a neural network could
be built which has the capacity to think as well as a person does for two
reasons.  Neural networks are terrible at deduction, or logical thinking and
the human brain is just too complex to completely simulate.  Also, some
problems are too difficult for present technology.  Real vision, for
example, is a long way off.</P>
<P>In short, Neural Networks are poor at precise calculations, but good at
association, evaluation, and pattern recognition.</P>
<P>
<HR>
<H1><A NAME="examples">EXAMPLES</A></H1>
<P>Included are several example files in the ``examples'' directory from the
distribution ZIP file. Each of the examples includes a short explanation 
at the top of the file. Each of these are ment to demonstrate simple, yet 
practical (for the most part :-) uses of this module.</P>