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DTIC ADA264665: Training Neural Networks with Weight Constraints |
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by Defense Technical Information Center |
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Hardware implementation of artificial neural networks |
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imposes a variety of constraints. Finite weight |
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magnitudes exist in both digital and analog devices. |
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Additional limitations are encountered due to the |
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imprecise nature of hardware components. These |
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constraints can be overcome with a stochastic global |
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optimization strategy which effectively searches the |
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range of the weight space and is robust to quantization |
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and modeling errors. Evolutionary programming is proposed |
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as a solution to training networks with these |
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constraints. This work investigates the use of |
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evolutionary programming in optimizing a network with |
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weight constraints. Comparisons are made to the |
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backpropagation training algorithm for networks with both |
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unconstrained and hard-limited weight magnitudes. Neural |
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networks, Analog, Digital, Stochastic |
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Date Published: 2018-03-10 08:25:18 |
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Identifier: DTIC_ADA264665 |
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Item Size: 9282739 |
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Language: english |
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Media Type: texts |
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# Topics |
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DTIC Archive; McDonnell, John R ; NAV... |
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