Proceedings Paper
Training product unit neural networks with genetic algorithms| Format | Member Price | Non-Member Price |
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Paper Abstract
This paper discusses the training of product neural networks using genetic algorithms. Two unusual techniques are combined; product units are employed in addition to the traditional summing units and a genetic algorithm is used to train the network rather than using backpropagation. As an example, a neural network is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima can affect the performance of a genetic algorithm, and one method of overcoming this is presented.
Paper Details
Date Published: 20 August 1992
PDF: 7 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139958
Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)
PDF: 7 pages
Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139958
Show Author Affiliations
David J. Janson, Univ. of Idaho (United States)
James F. Frenzel, Univ. of Idaho (United States)
Published in SPIE Proceedings Vol. 1706:
Adaptive and Learning Systems
Firooz A. Sadjadi, Editor(s)
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