A Novel Method for Radio Frequency Identification (RFID) Multi-tag Structure Prediction Based on a Chaos Algorithm and Laser Ranging
X-L. Yu, Z-L. Liu, L. Li, Z-M. Zhao and R-D. Ji
In order to test the dynamic performance of radio frequency identification (RFID) a dynamic measurement distance system based on laser ranging is proposed, alongside a back propagation (BP) neural network with improved particle swarm optimization (PSO) to predict the RFID reading distance. The chaos algorithm is used to update the inertia weight of the particle swarm, so that the particle swarm has higher global search ability. The opposition learning is used to improve the particle diversity and prevent the local optimal solution. This paper designs a detection system that uses laser ranging sensors to measure the coordinates and the tags reading distance. The experiment trains the RFID multi-tag distribution and predicts the tags reading distance. The experimental results show that the algorithm can effectively improve the accuracy and robustness of network prediction. It provides ideas and methods for the optimal distribution of RFID tags.
Keywords: Laser ranging system, radio frequency identification (RFID), radio frequency identification (RFID) multi-tag, chaos, opposition learning, particle swarm optimization (PSO), distribution, predictive performance