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Proceedings Paper

Multidimensional Kohonen net on a HyperCube
Author(s): Bruce A. Conway; Matthew Kabrisky; Steven K. Rogers; Gary B. Lamont
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Paper Abstract

This report details the implementation of the Kohonen Self-Organizing Net on a 32-node Intel iPSC/1 HyperCube and the 25 performance improvement gained by increasing the dimensionality of the net without increasing processing requirements. 1. KOHONEN SELF-ORGANIZING MAP IMPLEMENTED ON THE INTEL iPSC HYPERCUBE This report examines the implementation of a Kohonen net on the Intel iPSC/l HyperCube and explores the performance improvement gained by increasing the dimensionality of the Kohonen net from the conventional two-dimensional case to the n-dimensional case where n is the number of inputs to the Kohonen net. In this example the Kohonen net performance is improved by increasing the dimensionality of the net without increasing the number of weights or nodes in the net and without increasing processing requirements. Kohonen in his Tutorial/ICCN 1 2 states that the dimensionality of the grid is not restricted to two but that maps in the biological brain tend to be two-dimensional. It is proposed that this is not a particularly severe restriction in the brain where not all inputs are connected to all nodes and specific maps can be formed for specific functions but in the case of the massively connected Kohonen net reducing all problems to two dimensions places an unnecessary burden on the learning process and necessarily causes the loss of information regarding the interrelationship of inputs and corresponding output clusters. Indeed reducing the dimension is a projection

Paper Details

Date Published: 1 August 1990
PDF: 7 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21178
Show Author Affiliations
Bruce A. Conway, U.S. Air Force Institute of Te (United States)
Matthew Kabrisky, U.S. Air Force Institute of Te (United States)
Steven K. Rogers, U.S. Air Force Institute of Te (United States)
Gary B. Lamont, U.S. Air Force Institute of Te (United States)


Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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