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Incremental echo state network for a water-jet propulsion USV: Theoretical and experimental research
Li DC(李德才); He YQ(何玉庆)
作者部门机器人学研究室
会议名称2016 American Control Conference, ACC 2016
会议日期July 6-8, 2016
会议地点Boston, MA, United states
会议主办者Adaptics; et al.; GE Global Research; MathWorks; Mitsubishi Electric Research Laboratory (MERL); Quanser
会议录名称Proceedings of the American Control Conference
出版者IEEE
出版地NEW YORK
2016
页码5296-5301
收录类别EI ; CPCI(ISTP)
EI收录号20164302948130
WOS记录号WOS:000388376105059
产权排序1
ISSN号0743-1619
ISBN号978-1-4673-8682-1
摘要In this paper, a new system identification method is proposed. Since the new method combines the advantage of Kalman filter, l1 norm regularization and echo state mechanisms, it termed as ESNKF-l1. The ESNKF-l1 inherits the basic idea of ESN learning with Kalman filter to update model parameters online and utilize l1 norm regularization to obtain a sparse model structure. Moreover, the training of the ESNKF-l1 is facilitated by employing a bound optimization algorithm, based on which, a proper surrogate function is defined and the l1 norm regularization is approximated by l2 norm, while remaining constrains to the model parameters. It lead to an efficient method for model parameters estimation, which can be solved by using pseudo-observation (PO) method. In this case, the l2 norm is incorporated into the filtering process by designing a fictitious measurement function and the model parameters can be updated in an iteration manner. Experimental results show that the proposed method is suitable to capture the dynamics of USV and is superior to existing methods.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/19364
专题机器人学研究室
通讯作者Li DC(李德才)
作者单位Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
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Li DC,He YQ. Incremental echo state network for a water-jet propulsion USV: Theoretical and experimental research[C]//Adaptics; et al.; GE Global Research; MathWorks; Mitsubishi Electric Research Laboratory (MERL); Quanser. NEW YORK:IEEE,2016:5296-5301.
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