Description
-
[10 points] Consider a neural network with two inputs and three neurons in the competitive layer. The input vectors in the training set have the values
-
x
= −1 , x
2
1
0
|
= 0 , x |
3 |
|
|
1 |
||
=
1/
1/
|
2 |
|
|
2 |
|
,
and the initial weight vectors are
-
w = 0
, w =
− 2 /
5
, w
1
2
3
−1
5
1/
a) Plot the input vectors and initial weights on a unit circle.
=
|
−1/ |
5 |
||
|
2 / |
5 |
||
.
b) Calculate the resulting weights found after training the neurons with competitive learning rule using
|
learning rate =0.5, on the following sequence of inputs: |
x |
, x |
2 |
, x |
, x |
, x |
2 |
, x . |
Note: Weights must always |
|
1 |
3 |
1 |
3 |
lie on a unit circle, and thus must be re-normalized after each iteration.
c) Analyze the resulting weights and elaborate on the final weight distribution with respect to the input vectors.
