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Proceedings Paper

Prestructuring ANNs via a priori knowledge
Author(s): George G. Lendaris; Karl Mathia
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Paper Abstract

The work to be described has as its objective development of a constructive method that uses certain a priori information about a problem domain to design the starting structure of an artificial neural network (ANN). For the prestructuring process, there is motivation to move away from homogeneous structures to ones that comprise modules of smaller ANNs. Issues of concern include physical realizability, scalability of training time with large numbers of connections, and successful generalization. The method explored is based on a general systems theory methodology (here called GSM) that calculates a kinds of structural information of the problem domain via analyzing I/O pairs from that domain. This GSM-based information is used for developing a modularized ANN starting structure. Extensive experiments on 3-input, 1-output Boolean mappings verify our predictions. In addition, the experiments indicate that the GSM-based modularized-ANN design is `conservative' in the sense that the PS of the modularized ANN contains at least all the mappings included in the GSM category used to design the ANN. Experiments with 5-input, 1-output Boolean functions provide further support of the conclusions.

Paper Details

Date Published: 6 April 1995
PDF: 1 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205120
Show Author Affiliations
George G. Lendaris, Portland State Univ. (United States)
Karl Mathia, Portland State Univ and Accurate Automation Corp. (United States)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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