Computational Intelligence - November 2015 - 46

Computer simulation was performed to demonstrate
learning and clustering by the neuron and synapses of Figure 9. Initial values for the weights were chosen randomly,
independently, with uniform probability between 0 and 1.
There were 50 excitatory and 50 inhibitory weights. There
were 50 training patterns whose vector components were
chosen randomly, independently, with uniform probability
between 0 and 1. Initially some of the input patterns produced positive (SUM) values, indicated in Figure 11(a) by
blue crosses, and the remaining patterns produced negative

(SUM) values, indicated in Figure 11(a) by red crosses. After
100 iterations, some of the reds and blues have changed sides,
as seen in Figure 11(b). After 2000 iterations, as seen in Figure 11(c), clusters have began to form and membership of the
clusters has stabilized. There are no responses near zero. After
5000 iterations, tight clusters have formed as shown in Figure  11(d). At the neuron output, the output of the half sigmoid, the responses will be binary, 0's and approximate 1's.
Upon convergence, the patterns selected to become 1's or
those selected to become 0's are strongly influenced by the

(Out)

(Out)

(Sum)

(Sum)

(a) Initial Responses.

(b) Responses After 100 Iterations.

(Out)

(Out)

(Sum)

(Sum)

(c) Responses After 2000 Iterations.

(d) Responses After 5000 Iterations.

0.2

MSE

0.15

0.1

0.05

0

0

1000

2000

3000

Iteration
(e) A Learning Curve.
Figure 11 A learning experiment.

46

IEEE ComputatIonal IntEllIgEnCE magazInE | novEmbEr 2015

4000

5000



Table of Contents for the Digital Edition of Computational Intelligence - November 2015

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