Proceedings Article10.1109/ICEICT.2019.8846256
Recursive Kernel MPE Loss Algorithm
Wentao Ma,Jinzhe Qiu +1 more
- 01 Jan 2019
4
TL;DR: A novel recursive adaptive filtering algorithm via the KMPE loss to identify linear system parameters under non-Gaussian noise cases and can obtain higher steady-state accuracy and faster convergence rate compared with some other existing algorithms.
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Abstract: Kernel mean p-power error (KMPE) as a robust learning loss has been successfully employed to design robust PCA and ELM. This paper proposes a novel recursive adaptive filtering algorithm via the KMPE loss to identify linear system parameters under non-Gaussian noise cases. To derive the recursive KMPE algorithm, a KMPE loss with a forgetting factor is given first, and then the gradient method is employed to derive a recursive form of the weight estimation with a gain matrix for the system. Numerical simulation results demonstrate that the proposed algorithm with a suitable p value can obtain higher steady-state accuracy and faster convergence rate compared with some other existing algorithms.
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Citations
Information Theoretic Learning.
Jose C. Principe
- 01 Jan 2005
TL;DR: This work states that there is information in the error signal that is not captured during the training of nonlinear adaptive systems under non-Gaussian distribution conditions when one insists on secondorder statistical criteria.
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