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Proceedings Paper

Hybrid neural networks and their application to particle accelerator control
Author(s): Emile Fiesler; Shannon R. Campbell
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Paper Abstract

We have tested several predictive algorithms to determine their ability to learn from and find relationships between large numbers of variables. The purpose of this test is to produce control algorithms for sophisticated devices like particle accelerators. In particular we use COMFORT, a particle accelerator simulator, to generate large amounts of data. We then compared results among several fundamentally different types of algorithms, including least squares and hybrid neural networks. Our data indicate which algorithms perform the best on the basis of performance and training times.

Paper Details

Date Published: 1 November 1999
PDF: 11 pages
Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); doi: 10.1117/12.367689
Show Author Affiliations
Emile Fiesler, Physical Optics Corp. (United States)
Shannon R. Campbell, Physical Optics Corp. (United States)


Published in SPIE Proceedings Vol. 3812:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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