2 September 1993 Performance evaluation of a neural network for weapon-to-target assignment
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This paper describes a neural network for assigning weapons to targets and compares its execution time on four distinct machines. The network employs more than 46,000 neural elements and more than 49 million connections. It has produced excellent results for a realistic test scenario. Not only has the neural network produced high quality assignments for a realistic test scenario, the neural approach can potentially deliver results in real-time. The machines employed to evaluate the execution speed of the neural algorithm for assigning weapons to targets were: a DEC VAX 8810, a Neural Emulation Tool (NET) neural network accelerator from Loral Corporation, an Intel iPSC/2 Hypercube and a Cray Y-MP4/464.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
J. Fury Christ, J. Fury Christ, Edward W. Page, Edward W. Page, Gene A. Tagliarini, Gene A. Tagliarini, "Performance evaluation of a neural network for weapon-to-target assignment", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152570; https://doi.org/10.1117/12.152570

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