Prediction of Passive UHF RFID´s Discrimination Based on LVQ Neural Network Method

Author

Li Bing ; He Yigang ; She Kai ; Hou Zhouguo ; Zhu Yanqing ; Guo Fengming

Author_Institution

Coll. of Electr. & Inf. Eng., Hunan Univ., Changsha, China

fYear

2010

fDate

23-25 Sept. 2010

Firstpage

1

Lastpage

4

Abstract

The discrimination of passive Ultra High Frequency (UHF) Radio Frequency Identification (RFID) system can be affected by several factors. To measure the discrimination of UHF RFID systems, a measurement system based on virtual instruments which can adjust the speed, position and angle of tag is built in this paper. The learning vector quantization neural network based on genetic algorithm (GA-LVQ) is introduced to predict the discrimination of UHF RFID systems. To enhance the searching efficiency, the GA is modified adaptively. Prediction results are found to be good in agreement with experimental data.

Keywords

genetic algorithms; learning (artificial intelligence); neural nets; radiofrequency identification; telecommunication computing; vector quantisation; virtual instrumentation; LVQ neural network method; genetic algorithm; learning vector quantization neural network; measurement system; passive UHF RFID discrimination; ultrahigh frequency radio frequency identification; virtual instruments; Artificial neural networks; Biological cells; Classification algorithms; Gallium; Radiofrequency identification; Support vector machine classification; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Wireless Communications Networking and Mobile Computing (WiCOM), 2010 6th International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-3708-5

Electronic_ISBN

978-1-4244-3709-2

Type

conf

DOI

10.1109/WICOM.2010.5601198

Filename

5601198