Collapsing multiple hidden layers in feedforward neural networks to a single hidden layer

Jeffrey L. Blue; Lawrence O. Hall

doi:10.1117/12.235964

22 March 1996 Collapsing multiple hidden layers in feedforward neural networks to a single hidden layer

Jeffrey L. Blue, Lawrence O. Hall

Proceedings Volume 2760, Applications and Science of Artificial Neural Networks II; (1996) https://doi.org/10.1117/12.235964
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

Feed forward neural networks are often configured with multiple hidden layers. The relative simplicity of a single hidden layer may allow the use of a broader number of algorithms than could be used with multiple hidden layers. The hardware mapping may also be simplified with all networks implemented in a single hidden layer. An algorithm is presented which will collapse a network to a single hidden layer. The algorithm replaces links between hidden layers with new units whose links bypass hidden layers. These new units have link weights calculated so that the new unit approximates the replaced link's contribution to the network. Trials with a variety of configurations and data sets demonstrate the validity and effectiveness of the concept.

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Jeffrey L. Blue and Lawrence O. Hall "Collapsing multiple hidden layers in feedforward neural networks to a single hidden layer", Proc. SPIE 2760, Applications and Science of Artificial Neural Networks II, (22 March 1996); https://doi.org/10.1117/12.235964

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KEYWORDS

Neural networks

IRIS Consortium

Binary data

Algorithm development

Iris

Computer engineering

Computer hardware

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