基于CPSO-Elman神经网络矿井下可见光定位  

Visible light localization under mine based on CPSO-Elman neural network

作　　者：高欣欣 王凤英[1] 秦岭 胡晓莉[1] GAO Xinxin;WANG Fengying;QIN Ling;HU Xiaoli(School of Information Engineering,Inner Mongolia University of Science and Technology,Baotou 014010,China)

机构地区：[1]内蒙古科技大学信息工程学院,内蒙古包头014010

出　　处：《传感器与微系统》2024年第6期122-124,128,共4页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(62161041);内蒙古关键技术攻关项目(2021GG0104);内蒙自然科学基金资助项目(2022MS06012)。

摘　　要：针对传统矿井下定位方法精度偏低问题,提出一种混沌粒子群优化(CPSO)Elman神经网络矿井下可见光定位系统。由于Elman神经网络在初始化时存在参数设置的随机性导致预测精度不高,采用CPSO算法优化Elman神经网络,选取适合的各层的初始权值和阈值,用于提高神经网络拓扑的稳定性。仿真结果表明:在3.6 m×3.6 m×3.6 m的环境里,本文所提的算法的平均定位误差达到3.70 cm,最大定位误差为26.54 cm,在实验阶段的平均定位误差为5.91 cm,最大定位误差为36.95 cm,能够满足煤矿井下定位需求。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.

关 键 词：可见光 矿井下定位 混沌粒子群优化算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TN929.1[自动化与计算机技术—计算机科学与技术]