[1]付思源,王华东.和声搜索算法优化神经网络的无线网络室内定位[J].南京理工大学学报(自然科学版),2017,41(04):428.[doi:10.14177/j.cnki.32-1397n.2017.41.04.005]
　Fu Siyuan,Wang Huadong.Indoor positioning of wireless network based on harmonysearch algorithm optimizing neural network[J].Journal of Nanjing University of Science and Technology,2017,41(04):428.[doi:10.14177/j.cnki.32-1397n.2017.41.04.005]
点击复制

和声搜索算法优化神经网络的无线网络室内定位()
分享到：

《南京理工大学学报》（自然科学版）[ISSN:1005-9830/CN:32-1397/N]

卷:
41卷
期数:
2017年04期
页码:
428
栏目:
出版日期:
2017-08-31

文章信息/Info

Title:
Indoor positioning of wireless network based on harmonysearch algorithm optimizing neural network
文章编号:
1005-9830(2017)04-0428-06
作者:
付思源1; 王华东2
1.甘肃中医药大学 定西校区,甘肃 定西 743000; 2.周口师范学院 计算机科学与技术学院,河南 周口 466001
Author(s):
Fu Siyuan1; Wang Huadong2
1.Dingxi Campus,Gansu University of Chinese Medicine,Dingxi 743000,China; 2.School of Computer Science and Technology,Zhoukou Normal University,Zhoukou 466001,China
关键词:
无线网络室内定位;  压缩感知算法;  训练样本;  聚类分析;  和声搜索算法;  神经网络
Keywords:
wireless indoor positioning;  compressed sensing algorithm;  training samples;  mean clustering analysis;  harmony search algorithm;  neural network
分类号:
TP393
DOI:
10.14177/j.cnki.32-1397n.2017.41.04.005
摘要:
室内环境复杂多变,无线信号具有强烈的时变性,支持向量机存在定位效率低,神经网络参数难以确定等难题。为了改善无线网络室内的定位效果,提出了和声搜索算法优化神经网络的无线网络室内定位模型。首先收集无线网络定位的训练样本,采用压缩感知算法减少训练样本的规模,然后采用聚类算法对样本进行聚类分析,选择最有效的训练样本,最后采用和声搜索算法优化神经网络实现无线网络定位,并通过具体仿真对比实验测试了该算法的可行性。测试结果表明,该算法的定位效果可以满足无线网络的定位实际要求。
Abstract:
Indoor environment is complex and changeable,and wireless signal has strong time-varying.Support vector machine has low positioning efficiency while neural network is difficult to determine the parameter.In order to improve the positioning performance in wireless network,a novel wireless positioning algorithm based on harmony search algorithm optimizing neural network is proposed.Firstly,training samples of wireless network are collected and the size of training samples is reduced by a compressed sensing algorithm; secondly,clustering algorithm is used to cluster the samples; finally,harmony search algorithm is used to optimize neural network and feasibility is tested by simulation experiments.Test results show that the positioning results of the proposed algorithm can meet the actual requirements of wireless network positioning.

参考文献/References:

[1] Gu Yanying,Lo Anthony,Niemegeers Ignas.A survey of indoor positioning system for wireless personal networks[J].IEEE Communications Survey & Tutorials,2009,11(1):13-32.
[2]Altintas B,Serif T.Improving RSSI-based indoor positioning algorithm via k-means clustering[J].European Wireless,2011,27(8):681-685.
[3]Wang Jie,Gao Qinghua,Wang Hongyu,et al.Device-free localization with multi-dimensional wireless link information[J].IEEE Transactions on Vehicular Technology,2015,64(1):356-366.
[4]杨东勇,顾东袁,傅晓婕.一种基于RSSI相似度的室内定位模型[J].传感技术学报,2009,22(2):264-268.
Yang Dongyong,Gu Dongyuan,Fu Xiaojie.An indoor location algorithm base on RSSI-similarity degree[J].Journal of Sensors and Actuators,2009,22(2):264-268.
[5]陈兵,杨小玲.一种基于概率密度的WLAN接入点定位的算法[J].电子与信息学报,2015,37(4):855-862.
Chen Bing,Yang Xiaoling.A WLAN access point localization algorithm based on probability density[J].Journal of Electronics & Information Technology,2015,37(4):855-862.
[6]Kung H Y,Chaisit S,Phuong N T M.Optimization of an RFID location identification scheme based on the neural network[J].International Journal of Communication Systems,2015,28(4):625-644.
[7]李华亮,钱志鸿,田洪亮.基于核函数特征提取的室内定位模型研究[J].通信学报,2017,38(1):158-167.
Li Hualiang,Qian Zhihong,Tian Hongliang.Research on indoor localization algorithm based on kernel principal component analysis[J].Journal of Communications,2017,38(1):158-167.
[8]Ma L,Xu Y.Received signal strength recovery in green WLAT indoor positioning system using singular value thresholding[J].Sensors,2015,15(1):1292-1311.
[9]徐玉滨,邓志安,马琳.基于核直接判别分析和支持向量回归的 WLAN 室内定位模型[J].电子与信息学报,2011,33(4):896-901.
Xu Yubin,Deng Zhian,Ma Lin.WLAN indoor positioning algorithm based on KDDA and SVR[J].Journal of Electronics & Information Technology,2011,33(4):896-901.
[10]石柯,陈洪生,张仁同.一种基于支持向量回归的802.11无线室内定位方法[J].软件学报,2014,25(11):2636-2651.
Shi Ke,Chen Hongsheng,Zhang Rentong.Indoor location method based on support vector regression in 802.11 wireless environments[J].Journal of Software,2014,25(11):2636-2651.
[11]陈淼.基于多高斯混合模型的WLAN室内定位系统[J].华中科技大学学报(自然科学版),2012,40(4):67-71.
Chen Mian.WLAN indoor location system based on multi-gaussian mixture model[J].J Huazhong Univ of Sci & Tcch(Natural Scicncc Edition),2012,40(4):67-71.
[12]周锦,李炜,金亮,等.基于KNN-SVM算法的室内定位系统设计[J].华中科技大学学报(自然科学版),2015,43(增1):521-527.
Zhou Jin,Li Wei,Jin Liang,et al.Indoor positioning system based on KNN-SVM algorithm[J].J Huazhong Univ of Sci & Tcch(Natural Scicncc Edition),2015,43(Sup I):521-527.
[13]张勇,黄杰,徐科宇.基于PCA-LSSVR算法的WLAN室内定位方法[J].仪器仪表学报,2015,36(2):408-416.
Zhang Yong,Huang Jie,Xu Keyu.Indoor positioning algorithm for WLAN based on principal component analysis and least square support vector regression[J].Chinese Journal of Scientific Instrument,2015,36(2):408-416.
[14]倪志伟,尹道明,王力,等.基于和声搜索算法的知识即服务动态组合优化[J].计算机工程与应用,2012,48(32):212-217.
Ni Zhiwei,Yin Daoming,Wang Li,et a1.Harmony search algorithms for dynamic optimization of composite knowledge as a service[J].Computer Engineering and Applications,2012,48(32):212-217.
[15]刘春燕,王坚.基于几何聚类指纹库的约束KNN室内定位模型[J].武汉大学学报·信息科学版,2014,25(11):2636-2651.
Liu Chunyan,Wang Jian.A constrained KNN indoor positioning model based on a geometric clustering fingerprinting technique[J].Geomatics and information Science of Wuhan University,2014,25(11):2636-2651.
[16]龚阳,崔琛,余剑,等.基于RBF神经网络的室内定位模型研究[J].电子测量技术,2016,39(10):57-61.
Gong Yang,Cui Chen,Yu Jian,et al.Research on indoor location algorithm based on RBF neural network[J].Electronic Measurement Technology,2016,39(10):57-61.

备注/Memo

备注/Memo:
收稿日期:2017-01-24 修回日期:2017-05-11基金项目:国家自然科学基金(U1504613); 河南省高校科技创新团队计划(17IRTSTHN009)
作者简介:付思源(1980-),女,硕士,讲师,主要研究方向:计算机网络技术,E-mail:35273625@qq.com; 通讯作者:王华东(1977-),男,硕士,副教授,主要研究方向:计算机网络与通信,E-mail:46935563@qq.com。
引文格式:付思源,王华东.和声搜索算法优化神经网络的无线网络室内定位[J].南京理工大学学报,2017,41(4):428-433.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2017-08-31