[1]XU Tong,LING Youzhu,CHEN Mengyuan.A bio-inspired algorithm integrated with DGSOM neural network[J].CAAI Transactions on Intelligent Systems,2017,12(3):405-412.[doi:10.11992/tis.201704038]
Copy

A bio-inspired algorithm integrated with DGSOM neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 3 Page number: 405-412 Column: 学术论文—机器学习 Public date: 2017-06-25
Title:
A bio-inspired algorithm integrated with DGSOM neural network
Author(s):
XU Tong;  LING Youzhu;  CHEN Mengyuan
Anhui Key Laboratory of Electric Drive and Control, Anhui Polytechnic University, Wuhu 241000, China
Keywords:
RatSLAM model; DGSOM neural network; simultaneous localization and mapping; closed-loop detection; precision rate; recall rate
CLC:
TP242.6;TP183
DOI:
10.11992/tis.201704038
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.
References:
[1] KOHONEN T. Self-organized formation of topologically correct feature maps[J]. Biological cybernetics, 1982, 43(1): 59-69.
[2] MARTINETZ T M, BERKOVICH S G, SCHULTEN K J. Neural-gas network for vector quantization and its application to time-series prediction[J]. IEEE transactions on neural networks, 1993,4(4): 558-569.
[3] 尹峻松, 胡德文, 陈爽,等. DSOM:一种基于NO时空动态扩散机理的新型自组织模型[J].中国科学E辑:信息科学,2004,34(10): 1094-1109.YIN Junsong, HU Dewen, CHEN Shuang, et al. DSOM: a new self-organization model based on NO space-time dynamic diffusion mechanism[J]. Science in China ser. E information sciences, 2004,34(10): 1094-1109.
[4] 王春东, 虞鹤峰, 王怀彬. Grey self-organizing map based intrusion detection[J]. Optoelectronics letters, 2009, 5(1): 64-68.
[5] 于乃功,王丽.双目立体视觉和GSOM相结合的机器人自主地图构建方法[J].计算机测量与控制, 2011, 19(11): 2810-2813.YU Naigong, WANG Li. Autonomous mapping for robot using a combination of binocular stereo vision and GSOM[J]. Computer measurement and control, 2011, 19(11): 2810-2813.
[6] MILFORD M, GEORGE A. Featureless visual processing for SLAM in changing outdoor environments[J]. Springer tracts in advanced robotics, 2014: 569-583.
[7] MILFORD M J, PRASSER D P, WYETH G F. Effect of representation size and visual ambiguity on RatSLAM system performance[C]//Australasian Conference on Robotics and Automation. Australian Robotics and Automation Society (ARAA), 2006: 1-8.
[8] MILFORD M, SCHULZ R, PRASSER D, et al. learning spatial concepts from RatSLAM representations[J]. Robotics and autonomous systems, 2007, 55(5):403-410.
[9] MILFORD M, WYETH G, PRASSER D. RatSLAM on the edge: revealing a coherent representation from an overloaded rat brain[C]//Ieee/rsj International Conference on Intelligent Robots and Systems, IROS 2006, October 9-15, 2006, Beijing, China. 2006:4060-4065.
[10] 王丽娟,陶璨,张光伟,等.小鼠初级听皮层SOM中间神经元突触输入的时空特性研究[J]. 第三军医大学学报, 2017, 39(5): 417-422.WANG Lijuan, TAO Can, ZHANG Guangwei, et al. Spatial-temporal characteristics of synaptic inputs received by SOM+ interneurons in mouse primary auditory cortex[J]. Journal of third military medical university, 2017,39(5): 417-422.
[11] 于乃功,方略,罗子维,等.大鼠脑海马结构认知机理及其在机器人导航中的应用[J].北京工业大学学报,2017, 43(3): 434-442.YU Naigong, FANG Lue, LUO Ziwei, et al. Cognitive mechanism of rat hippocampal formation and its application in robot navigation[J]. Journal of Beijing university of technology, 2017, 43(3): 434-442.
[12] 刘帅师, 程曦, 郭文燕,等. 深度学习方法研究新进展[J]. 智能系统学报, 2016, 11(5):567-577.LIU Shuaishi, CHENG Xi, GUO Wenyan,et al. Progress report on new research in deep learning[J]. CAAI transactions on intelligent systems, 2016, 11(5):567-577.
[13] 丁永生.计算智能的新框架:生物网络结构[J]. 智能系统学报, 2007, 2(2): 26-30.DING Yongsheng. A new scheme for computational intelligence: bio-network architecture[J]. CAAI transactions on intelligent systems, 2007, 2(2): 26-30.
[14] 芮小平,张立强.基于SOM的多维信息可视化研究[J].应用基础与工程科学学报, 2011, 19(3): 379-388.RUI Xiaoping, ZHANG Liqiang. Multi-dimensional information visualization based on SOM[J]. Journal of basic science and engineering, 2011, 19(3): 379-388.
[15] 尹柏强,何怡刚,朱彦卿.一种广义S变换及模糊SOM网络的电能质量多扰动检测和识别方法[J].中国电机工程学报, 2015, 35(4): 866-872.YIN baiqiang, HE Yigang, ZHU Yanqing. Detection and classification of power quality multi-disturbances based on generalized S-transform and fuzzy SOM neural network[J]. Proceedings of the CSEE,2015,35(4):866-872.
[16] 於东军,吴小俊, HANCOCK Edwin R,等.广义SOM及其在人脸性别识别中的应用[J].计算机学报, 2011, 34(9): 1719-1725.YU Dongjun, WU Xiaojun, HANCOCK Edwin R, et al. Generalized SOM with application to facial gender identification[J].Chinese journal of computers, 2011, 34(9): 1719-1725.
[17] 刘艳,陈丽安.基于SOM的真空断路器机械故障诊断[J].电工技术学报, 2017,32(5):49-54.LIU Yan, CHEN Li’an. Mechanical fault diagnosis of vacuum circuit breaker based on SOM[J]. Transactions of China electrotechnical society, 2017, 32(5): 49-54.
[18] 邱天宇, 申富饶, 赵金熙. 自组织增量学习神经网络综述[J]. 软件学报, 2016, 27(9): 2230-2247.QIU Tianyu, SHEN Furao, ZHAO Jinxi. Review of self-organizing incremental neural network[J]. Journal of software, 2016, 27(9): 2230-2247
[19] 赵建华, 李伟华. 有监督SOM神经网络在入侵检测中的应用[J]. 计算机工程, 2012, 38(12): 110-111.ZHAO Jianhua, LI Weihua. Application of supervised SOM neural network in intrusion detection[J]. Computer engineer, 2012, 38(12): 110-111.
Similar References:

Memo

-

Last Update: 2017-06-25

Copyright © CAAI Transactions on Intelligent Systems