Creating a Self-Organizing MAP Neural Network (newsom) :: Self-Organizing and Learn. Vector Quant. Nets (Neural Network Toolbox)

Neural Network Toolbox

Creating a Self-Organizing MAP Neural Network (newsom)

You can create a new SOFM network with the function newsom. This function defines variables used in two phases of learning:

Ordering-phase learning rate
Ordering-phase steps
Tuning-phase learning rate
Tuning-phase neighborhood distance

These values are used for training and adapting.

Consider the following example.

Suppose that we want to create a network having input vectors with two elements that fall in the range 0 to 2 and 0 to 1 respectively. Further suppose that we want to have six neurons in a hexagonal 2-by-3 network. The code to obtain this network is

```
net = newsom([0 2; 0 1] , [2 3]);
```

Suppose also that the vectors to train on are

P = [.1 .3 1.2 1.1 1.8 1.7 .1 .3 1.2 1.1 1.8 1.7;...
0.2 0.1 0.3 0.1 0.3 0.2 1.8 1.8 1.9 1.9 1.7 1.8]

We can plot all of this with

plot(P(1,:),P(2,:),'.g','markersize',20)
hold on
plotsom(net.iw{1,1},net.layers{1}.distances)
hold off

to give

The various training vectors are seen as fuzzy gray spots around the perimeter of this figure. The initialization for newsom is midpoint. Thus, the initial network neurons are all concentrated at the black spot at (1, 0.5).

When simulating a network, the negative distances between each neuron's weight vector and the input vector are calculated (negdist) to get the weighted inputs. The weighted inputs are also the net inputs (netsum). The net inputs compete (compete) so that only the neuron with the most positive net input will output a 1.

Architecture Training (learnsom)