DocumentCode :
1754044
Title :
The Measurement for Permittivity of Materials Based on Artificial Nerve Network
Author :
Qian, Chen ; Ka-ma, Huang ; Ming, Luo ; Xiao-yong, Zeng
Author_Institution :
Sch. of Electron. & Inf. Eng., Sichuan Univ., Chengdu, China
Volume :
1
fYear :
2011
fDate :
28-29 March 2011
Firstpage :
272
Lastpage :
274
Abstract :
Effective complex permittivity measurements of materials are important in microwave engineering and microwave chemistry. Artificial neural network (ANN) computational module has been used in microwave technology and becomes a useful tool recently. A neural network can be trained to learn the behavior of an effective complex permittivity of material under microwave irradiation in a test system and it can provide a fast and accurate result for the permittivity of material. Thus, the on-line measurement has been realized. In this paper, a measurement system has been designed and the S-parameters are obtained by full-wave simulations to reconstruct the permittivity of material. Moreover, several organic solvents have been measured. The reconstructed results of the effective permittivities of solvents by means of the ANN agree well with previous published data.
Keywords :
S-parameters; electrical engineering computing; microwave devices; neural nets; permittivity measurement; S-parameters; artificial nerve network; artificial neural network; full wave simulations; materials permittivity measurement; microwave chemistry; microwave engineering; Artificial neural networks; Biomedical measurements; Image reconstruction; Materials; Microwave measurements; Permittivity; Permittivity measurement; Effective permittivity; Measurement; Neural network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on
Conference_Location :
Shenzhen, Guangdong
Print_ISBN :
978-1-61284-289-9
Type :
conf
DOI :
10.1109/ICICTA.2011.78
Filename :
5750608
Link To Document :
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