Hybrid neural networks and their application to particle accelerator control

Emile Fiesler; Shannon R. Campbell

doi:10.1117/12.367689

1 November 1999 Hybrid neural networks and their application to particle accelerator control

Emile Fiesler, Shannon R. Campbell

Author Affiliations +

Proceedings Volume 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II; (1999) https://doi.org/10.1117/12.367689
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

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.

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Emile Fiesler and Shannon R. Campbell "Hybrid neural networks and their application to particle accelerator control", Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); https://doi.org/10.1117/12.367689

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