Chinese Physics C > 2017, Vol. 41 > Issue(1): 017003 DOI: 10.1088/1674-1137/41/1/017003

Optimized feed-forward neural-network algorithm trained for cyclotron-cavity modeling

1.
Energy Engineering and Physics Department, Amirkarbir University of Technology, Tehran, 15857-4413 Iran
2.
College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, Korea

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Abstract：
The cyclotron cavity presented in this paper is modeled by a feed-forward neural network trained by the authors' optimized back-propagation (BP) algorithm. The training samples were obtained from simulation results that are for a number of defined situations and parameters and were achieved parametrically using MWS CST software; furthermore, the conventional BP algorithm with different hidden-neuron numbers, structures, and other optimal parameters such as learning rate that are applied for our purpose was also used here. The present study shows that an optimized FFN can be used to estimate the cyclotron-model parameters with an acceptable error function. A neural network trained by an optimized algorithm therefore shows a proper approximation and an acceptable ability regarding the modeling of the proposed structure. The cyclotron-cavity parameter-modeling results demonstrate that an FNN that is trained by the optimized algorithm could be a suitable method for the estimation of the design parameters in this case.
- cyclotron cavity,
- CST,
- modeling,
- neural network

References

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Masoumeh Mohamadian, Hossein Afarideh and Mitra Ghergherehchi. Optimized feed-forward neural-network algorithm trained for cyclotron-cavity modeling[J]. Chinese Physics C, 2017, 41(1): 017003. doi: 10.1088/1674-1137/41/1/017003

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Received: 2016-02-29
Revised: 2016-05-12

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通讯作者:陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

Optimized feed-forward neural-network algorithm trained for cyclotron-cavity modeling

Corresponding author:Hossein Afarideh,hafarideh@aut.ac.ir

Corresponding author:Mitra Ghergherehchi,hafarideh@aut.ac.ir

1. Energy Engineering and Physics Department, Amirkarbir University of Technology, Tehran, 15857-4413 Iran

2. College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, Korea

Received Date:2016-02-29

Accepted Date:2016-05-12

Available Online:2017-01-05

Abstract:The cyclotron cavity presented in this paper is modeled by a feed-forward neural network trained by the authors' optimized back-propagation (BP) algorithm. The training samples were obtained from simulation results that are for a number of defined situations and parameters and were achieved parametrically using MWS CST software; furthermore, the conventional BP algorithm with different hidden-neuron numbers, structures, and other optimal parameters such as learning rate that are applied for our purpose was also used here. The present study shows that an optimized FFN can be used to estimate the cyclotron-model parameters with an acceptable error function. A neural network trained by an optimized algorithm therefore shows a proper approximation and an acceptable ability regarding the modeling of the proposed structure. The cyclotron-cavity parameter-modeling results demonstrate that an FNN that is trained by the optimized algorithm could be a suitable method for the estimation of the design parameters in this case.

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Optimized feed-forward neural-network algorithm trained for cyclotron-cavity modeling

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