| Title |
Development of Optimized RF Cavity in 10 MeV Cyclotron |
| Authors |
- M. Mohamadian, H. Afarideh, M. Salehi
AUT, Tehran, Iran
- J.-S. Chai, M. Ghergherehchipresenter
SKKU, Suwon, Republic of Korea
|
| Abstract |
Cyclotron cavity modelled by an artificial neural net-work, which is trained by our optimized algorithm. The training samples are obtained from simulation results, which are done by MWS CST software for some defined situation and parameters, and also with the conventional BP algorithm. It is shown that the optimized FFN can estimate the cyclotron model parameters with acceptable outputs. Hence, the neural network trained by this algorithm represents the proper estimation and acceptable ability to our structure modelling. The cyclotron cavity parameter modelling illustrate that the neural network trained by this algorithm could be the acceptable method to design parameters.
|
| Paper |
download TUPMR011.PDF [0.489 MB / 3 pages] |
| Export |
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| Conference |
IPAC2016, Busan, Korea |
| Series |
International Particle Accelerator Conference (7th) |
| Proceedings |
Link to full IPAC2016 Proccedings |
| Session |
Poster Session |
| Date |
10-May-16 16:00–18:00 |
| Main Classification |
04 Hadron Accelerators |
| Sub Classification |
A13 Cyclotrons |
| Keywords |
network, cyclotron, cavity, simulation, resonance |
| Publisher |
JACoW, Geneva, Switzerland |
| Editors |
Christine Petit-Jean-Genaz (CERN, Geneva, Switzerland); Dong Eon Kim (PAL, Pohang, Republic of Korea); Kyung Sook Kim (PAL, Pohang, Republic of Korea); In Soo Ko (POSTECH, Pohang, Republic of Korea); Volker RW Schaa (GSI, Darmstadt, Germany) |
| ISBN |
978-3-95450-147-2 |
| Published |
June 2016 |
| Copyright |
|
