improved mobile robot's corridor-scene classifier based on probabilistic spiking neuron model

ISCAS OpenIR

	improved mobile robot's corridor-scene classifier based on probabilistic spiking neuron model
	Wang Xiuqing; Hou Zeng-Guang; Tan Min; Wang Yongji; Fu Siyao; Chen Lihui
	2011
会议名称	10th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2011
会议录名称	Proceedings of the 10th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2011
页码	348-355
会议日期	August 18,
会议地点	Banff, AB, Canada
收录类别	EI
ISBN	9781457716966
部门归属	(1) Hebei Normal University Shijiazhuang 050031 China; (2) Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100090 China; (3) Laboratory for Internet Technologies Institute of Software Chinese Academy of Sciences Beijing 100190 China; (4) Minzu University of China Beijing 100081 China
摘要	The ability of cognition and recognition for complex environment is very important for a real autonomous robot. A improved Corridor-Scene-Classifier based on probabilistic Spiking Neuron Model(pSNM) for mobile robot is designed. In the SNN classifier, the model pSNM is used. As network's training, Thorpe's learning rule is used. The experimental results show that the improved Classifier is more effective and it also has stronger robustness than the previous classifier based on Integrated-and-Fire (IAF) spiking neuron model for the structural corridor-scene. It also has better robustness than the traditional kernel-pca and the BP Corridor-Scene-classifier. © 2011 IEEE.; The ability of cognition and recognition for complex environment is very important for a real autonomous robot. A improved Corridor-Scene-Classifier based on probabilistic Spiking Neuron Model(pSNM) for mobile robot is designed. In the SNN classifier, the model pSNM is used. As network's training, Thorpe's learning rule is used. The experimental results show that the improved Classifier is more effective and it also has stronger robustness than the previous classifier based on Integrated-and-Fire (IAF) spiking neuron model for the structural corridor-scene. It also has better robustness than the traditional kernel-pca and the BP Corridor-Scene-classifier. © 2011 IEEE.
关键词	Information Science Mobile Robots
主办者	IEEE; IEEE Computer Society (CS); IEEE Computational Intelligence Society (CIS); University of Calgary; The IEEE ICCI Steering Committee
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16212
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Wang Xiuqing,Hou Zeng-Guang,Tan Min,et al. improved mobile robot's corridor-scene classifier based on probabilistic spiking neuron model[C],2011:348-355.