算法改进的自组织神经网络曲面重构  

Mesh surface reconstruction based on improved kohonen neural network

作　　者：吴雪梅[1,2] 于广滨[3] 赵永强[1] 胡长胜[1] 李瑰贤[1] 

机构地区：[1]哈尔滨工业大学机电工程学院,哈尔滨150001 [2]哈尔滨工业大学材料科学与工程学院博士后流动站,哈尔滨150001 [3]哈尔滨理工大学机械动力学院,哈尔滨150080

出　　处：《哈尔滨工业大学学报》2012年第5期63-65,共3页Journal of Harbin Institute of Technology

基　　金：国家自然科学基金资助项目(50905049);黑龙江省科技攻关项目(GC09A524);黑龙江省博士后基金(LBH-Z09189);黑龙江省国际合作项目(WB06A06)

摘　　要：为提高神经网络法三角网格曲面重构的效率,提出自组织神经网络算法与模糊聚类算法相结合的改进算法.应用改进算法对大规模散乱点云曲面及花瓶实例进行了网络训练及三角网格重建,在初次网络训练收敛后,加入模糊聚类计算模块,由模糊聚类算法中隶属度计算来确定输入样本是否可用.与自组织神经网络算法训练特性进行了比较,结果表明:改进后算法避免了以往算法的重复循环,减少了计算量,加快了网络训练收敛速度和三角网格曲面重构的速度.仿真重构结果表明:改进后的自组织神经网络算法可实现不同疏密程度的三角网格曲面重建,并可在保持原数据特征的前提下实现数据精简,较通常算法收敛速度快.To improve the efficiency of triangle mesh surface reconstruction in neural network,an improved Kohonen neural network is put forward,which combines Kohonen neural network and faintness clustering algorithm,and by which large scale scattered point clouds triangle mesh surface and vase surface reconstruction have been done.Characteristics comparison is carried out between the improved algorithm and general one,and the results show that the improved algorithm avoids repeat circulation in general algorithm,reduces calculation time,improves the efficiency and rate of the triangle mesh surface reconstruction.Simulation reconstruction result indicates that the improved arithmetic can realize sparse and dense triangle mesh surface reconstruction and data condensation under preconditions with primary data characteristics.The improved arithmetic has fast network convergence speed.

关 键 词：三角网格曲面重建 自组织神经网络 模糊聚类 

分 类 号：TH164[机械工程—机械制造及自动化]