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Proceedings Paper

Practical constraints pertinent to the design of neural networks
Author(s): Said Abdallah; Rufus H. Cofer
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Paper Abstract

in designing a feedforward neural network for numerical computation using the backpropagation algorithm it is essential to know that the resulting network has a practical global minimum, meaning that convergence to a stationary solution can be achieved in reasonable time and using a network of reasonable size. This is in contrast to theoretical results indicating that any square-integrable (L2) function can be computed assuming that an unlimited number of neurons are available. A class of problems is discussed that does not fit into this category. Although these problems are conceptually simple, it is shown that in practice convergence to a stationary solution can only be approximate and very costly. Computer simulation results are shown, and concepts are presented that can improve the performance by a careful redesign of the problem.

Paper Details

Date Published: 20 August 1992
PDF: 9 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139946
Show Author Affiliations
Said Abdallah, Florida Institute of Technology (United States)
Rufus H. Cofer, Florida Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)

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