Title :
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
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;
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
DOI :
10.1109/WICOM.2010.5601198
