=============================================================================
	README file for the example files eight_jordan.xxx
=============================================================================


Description:	Jordan network (partially recurrent network)
============	for the task to learn the shape of a lying figure '8'.

The task of this partially recurrent network is to predict the shape
of a lying figure '8'. The problem is described in detail in 

J.L. Elman: Finding Structure in Time. Cognitive Science, 14:179-211, 1990

The two input units code the (x, y)-Position of the current point of
the curve, the output units the (x', y')-Position of the next
point. Usually 16 points (patterns) are used to approximate the shape
of the figure 8, the central crossing point (0.5, 0.5) appearing
twice, depending on which direction the stroke takes. The difficulty
for the network arises from the input pattern of this central crossing
point for which the network must predict two different successors
(output patterns) depending on the previous point.

See the user manual for a detailed description of Jordan networks and
their usage.


Network-Files:	eight_jordan.net
==============

This network file contains a trained jordan network for the task to
predict the figure of a lying eight described above. The standard
configuration file for this network is eight_jordan.cfg (one 2D display
only).


Pattern-Files:	eight_016.pat
==============	eight_160.pat

The pattern files differ only in the number of patterns they contain,
indicated in the name of the pattern file. The larger file consists of
10 concatenations of the smaller one

Hints:
======

The easiest way to create Jordan or Elman networks is with the BIGNET
panel from the info panel. All network parameters can then be
specified in a special Jordan or Elman network creation panel called
with the respective button in the BIGNET panel.

If you want to train your own Elman or Jordan network from scratch,
note to set the proper initialization function and initialization
parameters.  In this example, we use the following values:
 1.0,  -1.0,  0.3,  1.0,  0.5  (5 parameters).

Remember to set the update function to JE_Order or JE_Special,
depending on your task (see the SNNS user manual for more details).

You may choose between four different learning functions. They are
given here with some values for the learning parameters for which the
training is relatively fast

			1st	2nd	3rd	4th	5th
JE_BP (Backprop)	0.2
JE_BP_Momentum		0.2	0.5
JE_Quickprop		0.3	2.0	0.0001
JE_Rprop		0.1	50.0

The behaviour of this network can very nicely be visualized with the
network analyzer tool which can be called from the info panel with the
GUI button as ANALYZER. The proceed as follows:
Press ON and LINE (so that both buttons are highlighted) from the
buttons at the right.
Press SETUP and choose X-Y graph from the network analyzer setup panel.
Choose the following values for axis, min, max, unit, grid:
	x	0.0,  1.0,  11,  10
	y	0.0,  1.0,  12,  10
This specifies the display area to be [0, 1] x [0, 1] and the outputs
of neurons 11 and 12 for x and y (the output units of the jordan
network).
Choose m-test: 16 in this network analyzer setup panel to test 16
patterns in a multiple inputs test sequence (You may also choose to test
more or less input patterns.

Finally, press the button M-TEST to test the trained network for the
number of input patterns specified.


=============================================================================
	End of README file
=============================================================================
