1. A Neural Net For Terrain Classification
Jackie Soenneker

2. Overview of SOM Self-Organizing Map Neural Net Has a grid of neurons
Each neuron has a weight vector
For each input vector there is a “winning” neuron
The winning neuron and its neighbors are adjusted to better match the input
1 2 3
7 8 9

3. Dimensions & Distance Functions
Dimension: how many neurons to use
Default is 4x6 (24 neurons)
I’m using twice as many neurons as terrain classes
Distance Function: how far apart are 2 neurons?
Link Distance (default) – number of links between the neurons
Euclidean Distance – straight-line distance between the neurons
Manhattan Distance – “follow the grid” distance between the neurons’ vectors

4. Topologies Topology: how are the neurons connected?
Topology doesn’t seem to effect learning very much
Hextop is the default and the one I’m using
Hextop
Gridtop
Randtop

5. Learning Phases SOM learning has two phases
Ordering Phase (first phase)
large learning rate
quickly fits the neurons to the general distribution of the input space
There are 2 Ordering Phase parameters
Learning rate – 0.9 (default)
Number of steps – 1,000 (default); 2,000 works better
The number of OP steps should probably grow proportionally to the number of neurons

6. Learning Phases con. Tuning Phase (second phase)
small learning rate
fine-tunes the neurons to fit the input space more precisely
There is one Tuning Phase parameter
Learning rate – 0.02 (default)

7. Summary Dimension: twice as many neurons as terrain classes
Distance Function: Link Distance (default)
Topology: Hextop (default)
OP Learning Rate: 0.9 (default)
OP Steps: 2,000 (probably increase w/ more nodes)
TP Learning Rate: 0.02 (default)