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

Masoumeh Mohamadian; Hossein Afarideh; Mitra Ghergherehchi

doi:10.1088/1674-1137/41/1/017003

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

[1]	M. S. Livingston, J. P. Blewett, Particle Accelerators (New York:McGraw-Hill, 1962)
[2]	J. Xia, Rusli, A. S. Kumta, IEEE T Plasma SCI, 38(2):142-148(2010)
[3]	M. Abd El-Kawy, M. Shaker Ismail, M. Abdel-Bary, M. M. Ouda, Arab J. Nucl. Science and App., 45(3):(2012)
[4]	R. Fletcher, Practical Methods of Optimization (UK:Chichester Wiley, 1987)
[5]	L. Fausett, Fundamentals of Neural Networks (New York:Prentice Hall 1994)
[6]	J. Y. F. Yam, T. W. S. Chow, IEEE T Neural Networks, 8:806-811, (1997)
[7]	J. L. McClelland, D. E. Rumelhart, Explorations in Parallel Distributed Processing-a Handbook of Models, Programs, and Exercises (MIT Press, Cambridge, 1988), p. 126-130
[8]	P. J. Werbos, The Roots of Backpropagation From Ordered Derivatives to Neural Networks and Political Forecasting (New York, NY:John Wiley Sons, 1994)
[9]	M. Mohamadian, H. Afarideh, F. Babapour, IAENG Int. J of Com. Science, 42(3):265-274(2015)
[10]	S. I. Gallant, Neural Network Learning and Expert Systems (MIT Press, Cambridge, 1993)
[11]	Y. Jongen et al, Proc of Cyclotrons, China (2010)
[12]	CST Studio Suite 2014(CST Microwave Studio)
[13]	M. Mohamadian, M. Salehi, H. Afarideh, M. Ghergherechi, J. Chai, Proc. 12th International Computational Accelerator Phys. Conf., China, (2015)
[14]	A. W. Chao, K. H. Mess, M. Tigner, F. Zimmermann, Handbook of Accelerator Physics and Engineering (2nd Edition, World Scientific Publishing Co., 2013)
[15]	S. L. Ozesmi, U. Ozesmi, Ecological Modelling, 116:15-31(1999)
[16]	O. M. Ibrahim, J Applied Sciences Research, 9(11):5692-5700(2013)
[17]	S. Tonidandel, J. M. LeBreton, J. B. Psychol, J Business and Psychology, 26(1):1-9(2011)
[18]	G. D. Garson, Artificial Intelligence Expert, 6:46-51(1991)
[19]	R. Gunawan, Y. Cao, L. Petzold, Biophys J., 88(4):2530-2540(2005)

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[20]	Chen Guoming , Chen Gang . Identifying τ Hadronic Decay with Neutral Network. Chinese Physics C, 1995, 19(S3): 247-256.

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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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