DocumentCode :
1592893
Title :
An improved BP neural network based on GA for 3D laser data repairing
Author :
Yu, Shouqian ; Rong, Lixia ; Chen, Weihai ; Wu, Xingming
Author_Institution :
Beijing University of Aeronautics & Astronautics
fYear :
2008
Firstpage :
571
Lastpage :
576
Abstract :
Affected by scanning object, environment, scanning speed and user¿s operation .etc, some information of the object¿s surface can¿t be detected by the laser scanner. Aiming at the data loss in laser detecting , the paper presents an improved BP neural network based on GA for 3D laser data repairing, the novelty of this method is adopting Genetic Algorithm(GA) to optimize the configure and weight of network, and at the same time combining Back Propagation(BP) Algorithm to find optimal approximation. The simulation shows the improved BP neural network based on GA has a faster constringency speed and better repairing precision than traditional BP neural network and GA algorithm. Lastly, the paper gives the result of repairing the point cloud collected by 3D information reconstruction system using this network
Keywords :
Approximation algorithms; Artificial intelligence; Clouds; Genetics; Machine vision; Neural networks; Object detection; Optimization methods; Surface emitting lasers; Turning; Data repairing, GA, BP network, Laser scanner;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Robotics, Automation and Mechatronics, 2008 IEEE Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-1675-2
Electronic_ISBN :
978-1-4244-1676-9
Type :
conf
DOI :
10.1109/RAMECH.2008.4690878
Filename :
4690878
Link To Document :
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