Earticle

초록

영어

The accuracy of indoor positioning algorithm has been the focus of research. In this paper, a particle swarm optimization algorithm based on particle swarm optimization algorithm and K -means algorithm is proposed. In this paper, firstly, the indoor positioning RFID model is constructed, and the positioning equation is constructed, then reduce the clustering algorithm to avoid human interference, through the K - means algorithm to form a particle swarm algorithm to initialize the particle swarm algorithm, finally, the particle swarm optimization algorithm is used to train all the parameters of RBF neural network, and then the optimal output model is obtained. Simulation results show that the algorithm can effectively improve the positioning accuracy, reduce energy consumption, and improve the positioning accuracy of 10%.

목차

Abstract
 1. Introduction
 2. Indoor Positioning RFID Model
 3. Constructing the Positioning Equation
 4. PSO-RBF Neural Network Model
  4.1. Subtractive Clustering Algorithm
  4.2. K-means Algorithm
  4.3. Particle Swarm Optimization Algorithm
  4.4. PSO-RBF Algorithm Model
 5. Target Location Algorithm based on PSO-RBF
 6. Simulation Experiment
 7. Conclusion
 References

키워드

저자

• Jiangang Jin [ Software Technology Vocational College, North China University of Water Resources and Electric Power, Zhengzhou 450045, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Future Generation Communication and Networking

• 간기

  격월간

• pISSN

  2233-7857

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605