MOP2WA03
SINAP, Shanghai, People's Republic of China
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MOP2WA03 |
The Feasibility of Neuron Network-based Beam-based Alignment | |
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| Artificial neuron networks which inspired by biological neural networks have been widely used in various domains, including computer vision, machine translation, pattern/speech recognition, medical diagnosis and so on, due to its overwhelming superiorities. But it's not until recently that intelligent algorithms have been introduced in light source field. M.P. Ehrlichman, Yi Jiao, Juhao Wu and A. Sanchez-Gonzalez did some work in this respect and got commendable results. Considering Shanghai X-ray Free-Electron Laser (SXFEL) conditions, we are urgent to improve the FEL performance, and fundamental technique turns out to be beam-based alignment. But it's difficult to implement this means in SXFEL due to the low electron beam energy resulting in uncontrollable orbit disturbance. Thus, a new method which is suitable for SXFEL is an eager desire. Here, we discuss the feasibility of neuron network-based beam-based alignment, and try to take it into reality in SXFEL. In fact, Hornik have proved, as early as 1989, that a single hidden layer feedforward networks can approximate any measurable function arbitrarily well, which provides the theoretical evidence to our suggestion. | ||
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Slides MOP2WA03 [5.439 MB] | |
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