Visible light localization under mine based on CPSO-Elman neural network
Aiming at problem of low precision of traditional underground mine positioning methods,a chaotic particle swarm optimization (CPSO )Elman neural network underground mine visible light positioning system is proposed.Due to the randomness of parameter setting during the initialization of Elman neural network,the prediction precision is not high.CPSO algorithm is used to optimize Elman neural network,and the appropriate initial weights and thresholds of each layer are selected to improve the stability of neural network topology.The simulation results show that in the environment of 3.6 m × 3.6 m × 3.6 m,the average positioning error of the proposed algorithm is 3.70 cm,and the maximum positioning error is 26.54 cm.In the experimental stage,the average positioning error is 5.91 cm,and the maximum positioning error is 36.95 cm,which can meet the positioning requirements of underground coal mine.
万方数据
维普
