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Proceedings Paper

AFIT neural network development tools and techniques for modeling articial neural networks
Author(s): Gregory L. Tarr; Dennis W. Ruck; Steven K. Rogers; Matthew Kabrisky
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Paper Abstract

Modeling of artificial neural networks is shown to depend on the programming decisions made in constructing the algorithms in software. Derivation of a common neural network training rule is shown including the effect of programming constraints. A method for constructing large scale neural network models is presented which allows for efficient use of memory hardware and graphics capabilities. Software engineering techniques are discussed in terms of design methodologies. Application of these techniques is considered for large scale problems including neural network segmentation of digital imagery for target identification. 1.

Paper Details

Date Published: 1 August 1990
PDF: 11 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21203
Show Author Affiliations
Gregory L. Tarr, U.S. Air Force Institute of Te (United States)
Dennis W. Ruck, U.S. Air Force Institute of Te (United States)
Steven K. Rogers, U.S. Air Force Institute of Te (United States)
Matthew Kabrisky, 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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