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- W2480333669 abstract "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." @default.
- W2480333669 created "2016-08-23" @default.
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- W2480333669 date "2016-07-01" @default.
- W2480333669 modified "2023-09-26" @default.
- W2480333669 title "Incremental echo state network for a water-jet propulsion USV: Theoretical and experimental research" @default.
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- W2480333669 doi "https://doi.org/10.1109/acc.2016.7526499" @default.
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