Neural Network Toolbox

newsom

Create a self-organizing map

Syntax

net = newsom

net = newsom(PR,[D1,D2,...],TFCN,DFCN,OLR,OSTEPS,TLR,TND)

Description

Competitive layers are used to solve classification problems.

net = newsom creates a new network with a dialog box.

net = newsom (PR,[D1,D2,...],TFCN,DFCN,OLR,OSTEPS,TLR,TND) takes,

• PR - R x 2 matrix of min and max values for R input elements.

  Di - Size of ith layer dimension, defaults = [5 8].

  TFCN - Topology function, default ='hextop'.

  DFCN - Distance function, default ='linkdist'.

  OLR - Ordering phase learning rate, default = 0.9.

  OSTEPS - Ordering phase steps, default = 1000.

  TLR - Tuning phase learning rate, default = 0.02;

  TND - Tuning phase neighborhood distance, default = 1.

and returns a new self-organizing map.

The topology function TFCN can be hextop, gridtop, or randtop. The distance function can be linkdist, dist, or mandist.

Properties

Self-organizing maps (SOM) consist of a single layer with the negdist weight function, netsum net input function, and the compet transfer function.

The layer has a weight from the input, but no bias. The weight is initialized with midpoint.

Adaption and training are done with trains and trainr, which both update the weight with learnsom.

Examples

The input vectors defined below are distributed over an two-dimension input space varying over [0 2] and [0 1]. This data will be used to train a SOM with dimensions [3 5].

• P = [rand(1,400)*2; rand(1,400)];
  net = newsom([0 2; 0 1],[3 5]);
  plotsom(net.layers{1}.positions)
  

Here the SOM is trained and the input vectors are plotted with the map that the SOM's weights have formed.

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

See Also

sim, init, adapt, train

newrbe

nncopy