智能系统学报2017，Vol.12Issue(3)：405-412,8.DOI:10.11992/tis.201704038
一种融合DGSOM神经网络的仿生算法研究
A bio-inspired algorithm integrated with DGSOM neural network
摘要
Abstract
Based on physiology and brain science, self-organizing-map (SOM) neural networks can learn and autonomously draw topological maps, but the initial SOM network structure must be repeatedly tested, so the real-time characteristics of the system cannot be assured.In this paper, we built a dynamic growing self-organizing map (DGSOM) based on direction and feature parameters that reduces network training times by the introduction of the direction parameter and decreases system complexity and avoids perceptual aliasing by the introduction of the feature parameter.By introducing the feature parameter, we can avoid perception confusion.We applied the proposed model to the view cells of the simultaneous localization and mapping system (SLAM) known as RatSLAM, proposed by Milford et al.Our experimental results show that the proposed DGSOM-RatSLAM model can decrease the complexity of the system by reducing the quantity of view cells and realize closed-loop detection earlier by matching the scene with view cells and detecting on the activity of the pose cells.We found the precision rate, recall rate, and F1 value of the DGSOM-RatSLAM model to reach 94.74%, 86.88%, and 90.64%, respectively, and those of the Gauss-DGSOM-RatSLAM model to reach 86.70%, 80.25%, and 83.35%, respectively.关键词
RatSLAM模型/DGSOM神经网络/同步定位与地图构建/闭环检测/准确率/召回率Key words
RatSLAM model/DGSOM neural network/simultaneous localization and mapping/closed-loop detection/precision rate/recall rate分类
信息技术与安全科学引用本文复制引用
许曈,凌有铸,陈孟元..一种融合DGSOM神经网络的仿生算法研究[J].智能系统学报,2017,12(3):405-412,8.基金项目
安徽高校自然科学研究项目(KJ2016A794). （KJ2016A794）
