基于二维GDSOM的路标动态自组织提取方法  

Dynamic Self-Organizing Landmark Extraction Method Based on 2-Dimensional Growing Dynamic Self-Organizing Feature Map

作　　者：王作为[1] 张汝波 

机构地区：[1]天津工业大学计算机科学与软件学院,天津300387 [2]哈尔滨工程大学计算机科学与技术学院,哈尔滨150001

出　　处：《模式识别与人工智能》2012年第6期1002-1006,共5页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60975071;60970016);国家863计划项目(No.2009AA04Z215)资助

摘　　要：提出一种基于距离传感器的结构化特征的动态、自组织提取方法.该方法由3个部分组成:主动感知行为的设计,时空信息的降维处理及路标的自组织提取.设计基于沿墙走的"主动感知行为"来获得高相关性的感知时空序列信息;给出基于变化检测和激活强度的活性神经元来对时空序列信息降维;最后提出一种二维动态增长自组织特征图方法,实现环境路标的自组织提取和识别.实验结果验证该方法的有效性.A dynamic self-organizing structural feature extraction method is presented based on distance sensor. The procedure consists of three parts： design of active exploration behavior, dimensionality reduction process of spatio-temporal information and self-organizing landmark extraction method. In this paper, active exploration behavior based on follow-wall is designed to obtain high correlative spatio-temporal sequence information. Activity neurons based on variety detection and activation intensity are used to reduce the dimensionality of spatio-temporal sequence. Finally, a method of 2-Dimensional growing dynamic self-organizing feature map （2-Dimensional GDSOM） is proposed to achieve self-organizing extraction and identification of environmental landmarks. The experimental results demonstrate the effectiveness of the method.

关 键 词：主动感知 感知-运动协调 自组织特征图 二维神经元网络 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]