基于遗传算法的改进BP神经网络用于三维激光数据修复

Shouqian Yu, Lixia Rong, Weihai Chen, Xingming Wu
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引用次数: 0

摘要

受扫描对象、环境、扫描速度和用户操作等因素的影响,激光扫描仪无法检测到物体表面的某些信息。针对激光检测中数据丢失的问题,提出了一种基于遗传算法的改进BP神经网络用于三维激光数据修复,该方法的新颖之处在于采用遗传算法优化网络的配置和权值,同时结合反向传播(BP)算法寻找最优逼近。仿真结果表明,基于遗传算法的改进BP神经网络比传统BP神经网络和遗传算法具有更快的收敛速度和更好的修复精度。最后给出了利用该网络对三维信息重建系统采集的点云进行修复的结果
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved BP neural network based on GA for 3D laser data repairing
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
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