Title :
The plastic self organising map
Author :
Lang, Robert ; Warwick, Kevion
Author_Institution :
Dept. of Cybern., Reading Univ., UK
fDate :
6/24/1905 12:00:00 AM
Abstract :
A novel extension to Kohonen´s self-organising map, called the plastic self organising map (PSOM), is presented. PSOM is unlike any other network because it only has one phase of operation. The PSOM does not go through a training cycle before testing, like the SOM does and its variants. Each pattern is thus treated identically for all time. The algorithm uses a graph structure to represent data and can add or remove neurons to learn dynamic nonstationary pattern sets. The network is tested on a real world radar application and an artificial nonstationary problem
Keywords :
graph theory; learning (artificial intelligence); pattern classification; self-organising feature maps; Kohonen self-organising map; graph structure; online training algorithm; passive radar; pattern classification; plastic self organising map; Artificial neural networks; Clustering algorithms; Cybernetics; Data visualization; Labeling; Neural networks; Neurons; Plastics; Radar applications; Testing;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005563