A Neural Net For Terrain Classification

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. Hextop Gridtop Randtop 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

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)