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A Neural Net For Terrain Classification
Jackie Soenneker
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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
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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
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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
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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
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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)
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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)
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