Title :
Doorplate Recognition for a Mobile Robot Based on PSO and RBF Neural Network
Author :
Zuo Guoyu ; Fan Yanfeng ; Qiao Junfei
Author_Institution :
Sch. of Electron. Inf. & Control Eng., Beijing Univ. of Technol., Beijing, China
Abstract :
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.
Keywords :
convergence; mobile robots; particle swarm optimisation; pattern clustering; radial basis function networks; robot vision; PSO; RBF neural network; accuracy recognition ratio; convergence rate; doorplate recognition; mixed optimization algorithm; mobile robot; particle swarm optimization; robot vision system; self-increasing clustering algorithm; Clustering algorithms; Function approximation; Mobile robots; Neural networks; Paper technology; Particle swarm optimization; Radial basis function networks; Robot vision systems; Robotics and automation; Signal processing algorithms; PSO algorithm; RBF neural network; doorplate recognition; mobile robot;
Conference_Titel :
Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
Conference_Location :
Changsha City
Print_ISBN :
978-1-4244-5001-5
Electronic_ISBN :
978-1-4244-5739-7
DOI :
10.1109/ICMTMA.2010.752
