JOURNAL OF COMPUTERS (JCP)

ISSN : 1796-203X

Volume : 3 Issue : 9 Date : September 2008

**Relation Organization of SOM Initial Map by Improved Node Exchange**

Tsutomu Miyoshi

Page(s): 77-84

Full Text: PDF (457 KB)

**Abstract**

The Self Organizing Map (SOM) involves neural networks, that learns the features of input data

thorough unsupervised, competitive neighborhood learning. In the SOM learning algorithm,

connection weights in a SOM feature map are initialized at random values, which also sets nodes at

random locations in the feature map independent of input data space. The move distance of output

nodes increases, slowing learning convergence. As precedence research, we proposed the

method to improve this problem, initial node exchange by using a part of feature map. In this paper,

we propose two improved exchange method, node exchange with fixed neighbor area and spiral

node exchange. The node exchange with fixed neighbor area uses fixed position of winner node

and fixed initial size of neighbor area that sets to cover whole feature map. We investigate how

average move distance of all nodes and average deviation of move distance would change with the

differences by type of fixed neighbor area in node exchange process. The spiral node exchange is

used instead of neighbor area reduction reputation of former method. By spiral node exchange,

repetition by node exchange process becomes needless and can expect speed up of total

processing.

**Index Terms**

first Self-organizing map, feature map, node exchange, fixed neighbor area, spiral exchange.

ISSN : 1796-203X

Volume : 3 Issue : 9 Date : September 2008

Page(s): 77-84

Full Text: PDF (457 KB)

thorough unsupervised, competitive neighborhood learning. In the SOM learning algorithm,

connection weights in a SOM feature map are initialized at random values, which also sets nodes at

random locations in the feature map independent of input data space. The move distance of output

nodes increases, slowing learning convergence. As precedence research, we proposed the

method to improve this problem, initial node exchange by using a part of feature map. In this paper,

we propose two improved exchange method, node exchange with fixed neighbor area and spiral

node exchange. The node exchange with fixed neighbor area uses fixed position of winner node

and fixed initial size of neighbor area that sets to cover whole feature map. We investigate how

average move distance of all nodes and average deviation of move distance would change with the

differences by type of fixed neighbor area in node exchange process. The spiral node exchange is

used instead of neighbor area reduction reputation of former method. By spiral node exchange,

repetition by node exchange process becomes needless and can expect speed up of total

processing.