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题名: A general closed-loop framework for multi-dimensional sequence processing
作者: Ding QC(丁其川) ; Zhao XG(赵新刚) ; Han JD(韩建达)
作者部门: 机器人学研究室
会议名称: 12th International Conference on Intelligent Autonomous Systems, IAS 2012
会议日期: June 26-29, 2012
会议地点: Jeju Island, Korea, Republic of
会议录: Intelligent Autonomous Systems 12 - Proceedings of the 12th International Conference, IAS 2012
会议录出版者: Springer Verlag
会议录出版地: Heidelberg, Germany
出版日期: 2012
页码: 311-321
收录类别: CPCI(ISTP) ; EI
ISSN号: 2194-5357
ISBN号: 9783642339318
关键词: Inverse kinematics ; State space methods
摘要: A novel closed-loop framework for multidimensional sequence processing is proposed in this paper. Traditional sequence-driven models are always forward, so no information is feedback to correct their outputs, which may deviate from the true values gradually due to the estimation error accumulating. To overcome the problem, the multidimensional vector in the input sequence is divided into two vectors based on its data attribute. One vector sequence generated from the original input sequence is considered as the new input sequence, and the other is considered as the measurement output sequence. The original output sequence is treated as the state sequence. Then, a closed-loop model in the state-space form is constructed, with which the states can be estimated online by filtering algorithms. The feasibility of the proposed framework has been verified by using the robot inverse kinematics. © 2013 Springer-Verlag.
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/10504
Appears in Collections:机器人学研究室_会议论文

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