The KOHONEN Learning Function

SOM training in SNNS can be performed with the learning function Kohonen. It can be selected from the list of learning functions in the remote panel. Five parameters have to be passed to this learning function:

• Adaptation Height (Learning Height) The initial adaptation height can vary between 0 and 1. It determines the overall adaptation strength.

• Adaptation Radius (Learning Radius) The initial adaptation radius is the radius of the neighborhood of the winning unit. All units within this radius are adapted. Values should range between 1 and the size of the map.

• Decrease Factor mult_H The adaptation height decreases monotonically after the presentation of every learning pattern. This decrease is controlled by the decrease factor mult_H:

• Decrease Factor mult_R The adaptation radius also decreases monotonically after the presentation of every learning pattern. This second decrease is controlled by the decrease factor mult_R:

• Horizontal size Since the internal representation of a network doesn't allow to determine the 2-dimensional layout of the grid, the horizontal size in units must be provided for the learning function. It is the same value as used for the creation of the network.

Note: After each completed training the parameters adaption height and adaption radius are updated in the remote panel to reflect their new values. So when training is started anew, it resumes at the point where it was stopped last. Both mult_H and mult_R should be in the range . A value of 1 consequently keeps the adaption values at a constant level.