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Title:
improved mobile robot's corridor-scene classifier based on probabilistic spiking neuron model
Author: Wang Xiuqing ; Hou Zeng-Guang ; Tan Min ; Wang Yongji ; Fu Siyao ; Chen Lihui
Source: Proceedings of the 10th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2011
Conference Name: 10th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2011
Conference Date: August 18,
Issued Date: 2011
Conference Place: Banff, AB, Canada
Keyword: Information science ; Mobile robots
Indexed Type: EI
ISBN: 9781457716966
Department: (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
Sponsorship: IEEE; IEEE Computer Society (CS); IEEE Computational Intelligence Society (CIS); University of Calgary; The IEEE ICCI Steering Committee
Abstract: 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.
English Abstract: 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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16212
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
Wang Xiuqing,Hou Zeng-Guang,Tan Min,et al. improved mobile robot's corridor-scene classifier based on probabilistic spiking neuron model[C]. 见:10th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2011. Banff, AB, Canada. August 18,.
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