Doorplate recognition for a mobile robot based on PSO and RBF neural network - 文章详情页

作者：

Zuo, Guoyu (Zuo, Guoyu.) (学者：左国玉) | Fan, Yanfeng (Fan, Yanfeng.) | Qiao, Junfei (Qiao, Junfei.) (学者：乔俊飞)

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EI Scopus

摘要：

This　paper　proposes　a　mixed　optimization　algorithm　based　on　RBF　neural　network　(RBF)　and　Particle　Swarm　Optimization　(PSO),　which　is　applied　to　the　doorplate　recognition　for　a　mobile　robot.　The　centers　and　widths　of　RBF　neural　network　are　determined　with　self-increasing　clustering　algorithm,　and　the　improved　particle　swarm　optimization　algorithm　is　used　to　optimize　their　distance　from　the　threshold.　Experimental　results　show　that　this　algorithm　has　an　advantage　over　traditional　neural　network　algorithm　in　terms　of　accuracy　recognition　ratio　and　convergence　rate.　Hence,　the　proposed　algorithm　can　meet　the　needs　of　robot　vision　system.　©　2010　IEEE.

关键词：

Clustering algorithms Mobile robots Particle swarm optimization (PSO) Radial basis function networks

作者机构：

[ 1 ] [Zuo, Guoyu]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 2 ] [Fan, Yanfeng]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 3 ] [Qiao, Junfei]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China

通讯作者信息：

电子邮件地址：

zuoguoyu@bjut.edu.cn

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