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1 November 1999 Hybrid neural networks and their application to particle accelerator control
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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