Simple Competitive Learning
by Ahmad Masadeh, Paul Watta,
and Mohamad Hassoun (March 1998)
This is a program that impliments the simple
competetive learning process as described on page 103 in M.
of Artificial Neural Networks (MIT
press, 1995 ). The
program uses equations 3.4.2 and 3.4.3 (page 103) for both Inner
Product and Euclidean Distance methods respectvely. It also uses
equation 3.4.5 (page 104) for updating the weights. Each Initial
weight unit has a distinctive color, and the final clusters are
shown in a color matching to its winning unit.
How to use the applet:
Choose the method of training :
Inner product or Euclidian
distance from the list.
To insert a cluster of points
just click on the CREATE
TRAINING SET Button, then
click on the desired position on the screen(each cluster
contains 100 random points and uses a gaussian
distribition).Multiple clicking at different points
creates more complex clusters.
To insert an initial unit
weight click on the INITIAL
UNIT WEIGHTS Button, then on
the desired position on the screen.( you can have up to 5
In order to see how the weight
trajectories develop press the TRAIN
You will notice that the final
weight trajectory position is marked by a black star (*).
In order to see the final
distinctive clusters press the CALIBRATE
The CLEAR Button
is used for clearing the screen.
You may specify the learning
rate to any value by typing in the desired learning rate
( note that the learning rate should be a small positive
You may choose how many cycles
you want the program to cycle through the training set by
choosing from the Training Cycle list. Each training
cycle is equivalent to m presentations
(iterations) of input vectors from the training set where
m is the size of the training set. The training
vectors are chosen uniformly randomly from the training