1 August 1990 AFIT neural network development tools and techniques for modeling articial neural networks
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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.
© (1990) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gregory L. Tarr, Dennis W. Ruck, Steven K. Rogers, Matthew Kabrisky, "AFIT neural network development tools and techniques for modeling articial neural networks", Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21203; https://doi.org/10.1117/12.21203
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KEYWORDS
Neural networks

Artificial neural networks

Visualization

Computer programming

Chemical elements

Image segmentation

Data modeling

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