Characterisation of the plasma density with two artificial neural network models

doi:10.1088/1674-1056/19/7/070505

中国物理B ›› 2010, Vol. 19 ›› Issue (7): 70505-070505.doi: 10.1088/1674-1056/19/7/070505

Characterisation of the plasma density with two artificial neural network models

高向东¹, 王腾², 李炜³

(1)Faculty of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510090, China; (2)Faculty of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510090, China;School of Computer, South China Normal University, Guangzhou 510631, China; (3)School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China

收稿日期:2009-04-25 修回日期:2009-08-04 出版日期:2010-07-15 发布日期:2010-07-15
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 60375012).

Characterisation of the plasma density with two artificial neural network models

Wang Teng (王腾)^ab, Gao Xiang-Dong (高向东)^a, Li Wei (李炜)^c

^a Faculty of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510090, China; ^b School of Computer, South China Normal University, Guangzhou 510631, China; ^c School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China

Received:2009-04-25 Revised:2009-08-04 Online:2010-07-15 Published:2010-07-15
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 60375012).

摘要/Abstract

摘要： This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is the target output neuron: the plasma density. The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe. The effectiveness of two artificial neural network models are demonstrated, the results show good agreements with corresponding experimental data. The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded, and the radial based function is more suitable than the multi layer perceptron in this work.

Abstract: This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is the target output neuron: the plasma density. The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe. The effectiveness of two artificial neural network models are demonstrated, the results show good agreements with corresponding experimental data. The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded, and the radial based function is more suitable than the multi layer perceptron in this work.

Key words: plasma density, prediction, multi layer perceptron, radial based function

中图分类号: (Ionization of plasmas)

52.25.Jm

07.05.Mh (Neural networks, fuzzy logic, artificial intelligence) 52.70.Ds (Electric and magnetic measurements) 52.77.Dq (Plasma-based ion implantation and deposition) 52.50.Sw (Plasma heating by microwaves; ECR, LH, collisional heating)

王腾, 高向东, 李炜. Characterisation of the plasma density with two artificial neural network models[J]. 中国物理B, 2010, 19(7): 70505-070505.

Wang Teng (王腾), Gao Xiang-Dong (高向东), Li Wei (李炜). Characterisation of the plasma density with two artificial neural network models[J]. Chin. Phys. B, 2010, 19(7): 70505-070505.

参考文献 21

[1]	Toader E I 2004 Plasma Sources Sci. Technol. bf 13 646
[2]	Chen J F and Ren Z X 1999 Vacuum 52 411
[3]	Mayuko K and Hiroshi M 2006 Vacuum 80 771
[4]	Jin X Y, Qiu X J and Zhu Z Y 2006 Acta Phys. Sin. 55 5338 (in Chinese)
[5]	Wang L, Cao J X, Wang Y, Niu T Y, Wang G and Zhu Y 2007 it Acta Phys. Sin. 56 1429 (in Chinese)
[6]	Hiroshi M, Doan H T and Yoshinobu K 2005 Surf. Coat. Technol. 200 850
[7]	Musil J 1996 Vacuum 47 145
[8]	Wang Y S, Sun J, Wang C J and Fan H D 2008 Acta Phys. Sin. 57 6120 (in Chinese)
[9]	Wang H, Wang A K, Yang Q W, Ding X T, Dong J Q, Sanuki H and Itoh K 2007 Chin. Phys. 16 3738
[10]	Du X L, Chen G C, Jiang D Y, Yao X Z and Zhu H S 1999 Acta Phys. Sin. 48 257 (in Chinese)
[11]	Dorteoust, Wu H M and Graves D B 1994 Plasma Sources Sci. Technol. 3 25
[12]	Lungu C P and Iwasak K 2002 Vacuum 66 197
[13]	Wu H M, Graves D B and Porteos R K 1995 Plasma Sources Sci. Technol. 4 22
[14]	Sterjovski Z and Nolan D 2006 J. Mater. Proc. Technol. 170 536
[15]	Xu L J and Xing J D 2007 Mater. Des. 28 1425
[16]	Bezerra E M and Ancelotti A C 2007 Mater. Sci. Eng. A 464 177
[17]	Scott D J and Coveney P V 2007 J. Eur. Cerma. Soc. bf 27 4425
[18]	Ming D J, Chyuan D L and Wang J T 2005 Appl. Surf. Sci. 245 290
[19]	Zhang G and Guessasma S 2006 Surf. Coat. Technol. bf 200 2610
[20]	Hakan C and Hasan O 2006 Wear 261 064
[21]	Pankaj S and Deo M C 2007 Applied Soft Computing 7 968

Characterisation of the plasma density with two artificial neural network models

Characterisation of the plasma density with two artificial neural network models

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

参考文献 21

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	蔡帮煌, 宋慧敏, 贾敏, 吴云, 崔巍, 黄胜方. Experimental study on energy characteristics and ignition performance of recessed multichannel plasma igniter[J]. 中国物理B, 2020, 29(6): 65207-065207.
[2]	王思博, 于锦禄, 叶景峰, 李国华, 陈朝, 蒋陆昀, 古晨力. Ignition characteristics of pre-combustion plasma jet igniter[J]. 中国物理B, 2019, 28(11): 114702-114702.
[3]	顾新杨, 王可嘉, 杨振刚, 刘劲松. Terahertz coherent detection via two-color laser pulses of various frequency ratios[J]. 中国物理B, 2019, 28(9): 98701-098701.
[4]	Ahmed Asaad I Khalil, Reem Al-Tuwirqi, Mohammed A Gondal, Noura Al-Suliman. Factors affecting improvement of fluorescence intensity of quartet and doublet state of NO diatomic molecule excited by glow discharge[J]. 中国物理B, 2018, 27(8): 85202-085202.
[5]	顾新杨, 刘劲松, 杨振刚, 汪盛烈, 王可嘉. Theoretical investigation of tunable polarized broadband terahertz radiation from magnetized gas plasma[J]. 中国物理B, 2018, 27(5): 58701-058701.
[6]	黄胜方, 宋慧敏, 吴云, 贾敏, 金迪, 张志波, 林冰轩. Experimental investigation on electrical characteristics and ignition performance of multichannel plasma igniter[J]. 中国物理B, 2018, 27(3): 35203-035203.
[7]	赵璐璐, 刘悦. Effects of gas pressure on plasma characteristics in dual frequency argon capacitive glow discharges at low pressure by a self-consistent fluid model[J]. 中国物理B, 2017, 26(12): 125201-125201.
[8]	刘悦, 赵璐璐, 周艳文. Effect of driving frequency on electron heating in capacitively coupled RF argon glow discharges at low pressure[J]. 中国物理B, 2017, 26(11): 115201-115201.
[9]	Ahmed Asaad I Khalil,Ashraf I Hafez,Mahmoud E Elgohary,Mohamed A Morsy. Tungsten ion source under double-pulse laser ablation system[J]. 中国物理B, 2017, 26(9): 95201-095201.
[10]	张文娟, 安宇. Abnormal ionization in sonoluminescence[J]. 中国物理B, 2015, 24(4): 47802-047802.
[11]	吴四清, 刘劲松, 汪盛烈, 胡兵. The dependence on optical energy of terahertz emission from air plasma induced by two-color femtosecond laser-pulses[J]. 中国物理B, 2013, 22(10): 104207-104207.
[12]	于锦禄, 何立明, 丁未, 王育虔, 杜纯. Impacts of air pressure on the evolution of nanosecond pulse discharge products[J]. 中国物理B, 2013, 22(5): 55201-055201.
[13]	司马文霞, 彭庆军, 杨庆, 袁涛, 施健. Local electron mean energy profile of positive primary streamer discharge with pin-plate electrodes in oxygen–nitrogen mixtures[J]. 中国物理B, 2013, 22(1): 15203-015203.
[14]	叶凡, 李正宏, 秦义, 蒋树庆, 薛飞彪, 杨建伦, 徐荣昆, 金永杰. Spatially-resolved spectroscopic diagnosing of aluminum wire array Z-pinch plasmas on QiangGuang-I facility[J]. 中国物理B, 2010, 19(7): 75204-075204.
[15]	卢洪伟, 胡立群, 周瑞杰, 林士耀, 钟国强, 王少锋, 陈开云, 许平, 张继宗, 凌必利, 毛松涛, 段艳敏. AC operation and runaway electron behaviour in HT-7 tokamak[J]. 中国物理B, 2010, 19(6): 65201-065201.