AFIT neural network development tools and techniques for modeling articial neural networks

Gregory L. Tarr; Dennis W. Ruck; Steven K. Rogers; Matthew Kabrisky

doi:10.1117/12.21203

1 August 1990 AFIT neural network development tools and techniques for modeling articial neural networks

Gregory L. Tarr, Dennis W. Ruck, Steven K. Rogers, Matthew Kabrisky

Proceedings Volume 1294, Applications of Artificial Neural Networks; (1990) https://doi.org/10.1117/12.21203
Event: 1990 Technical Symposium on Optics, Electro-Optics, and Sensors, 1990, Orlando, FL, United States

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.

Citation Download Citation

Gregory L. Tarr, Dennis W. Ruck, Steven K. Rogers, and 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); https://doi.org/10.1117/12.21203

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