• DocumentCode
    2514551
  • Title

    An effective method for RFID tag antenna optimization based on artifical neural network

  • Author

    Shang, Jiachuan ; Zhang, Ning ; Li, Xiuping

  • Author_Institution
    Key Lab. of Universal Wireless Commun., Beijing Univ. of Posts & Telecommun., Beijing, China
  • fYear
    2010
  • fDate
    28-30 Nov. 2010
  • Firstpage
    383
  • Lastpage
    386
  • Abstract
    Evolutionary algorithms combined with artificial neural network (ANN) have been applied in RFID tag antenna optimization platform. An effective method for RFID tag antenna optimization by particle swarm optimization (PSO) algorithm or genetic algorithm (GA) combined with ANN is presented in this paper. ANN is used to establish the non-linear model of tag antenna which is shown to be as accurate as an electromagnetic simulator and can be used for constructing the fitness function of PSO and GA. The PSO and GA optimizers are developed and executed in C++. Finally, this optimization method is turned out to be much more efficient than any electromagnetic simulator optimization. In addition, the PSO optimization results show that it is faster than GA.
  • Keywords
    C++ language; antennas; electrical engineering computing; genetic algorithms; neural nets; particle swarm optimisation; radiofrequency identification; C++; RFID tag antenna optimization; artificial neural network; electromagnetic simulator; evolutionary algorithms; genetic algorithm; nonlinear model; particle swarm optimization; Artificial neural networks; Dipole antennas; Gallium; Numerical models; Optimization; Radiofrequency identification; Genetic Algorithm; Neural Network; Optimization; Particle Swarm Optimization Algorithm; Tag Antennas;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Computing and Telecommunications (YC-ICT), 2010 IEEE Youth Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-8883-4
  • Type

    conf

  • DOI
    10.1109/YCICT.2010.5713125
  • Filename
    5713125