Online Sequential Extreme Learning Machine Based on M-estimator and Variable Forgetting Factor

GUO Wei; XU Tao; YU Jianjiang; TANG Keming

doi:10.11999/JEIT170800

Volume 40 Issue 6

May 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(6): 1360-1367

GUO Wei, XU Tao, YU Jianjiang, TANG Keming. Online Sequential Extreme Learning Machine Based on M-estimator and Variable Forgetting Factor[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1360-1367. doi: 10.11999/JEIT170800

Citation:

GUO Wei, XU Tao, YU Jianjiang, TANG Keming. Online Sequential Extreme Learning Machine Based on M-estimator and Variable Forgetting Factor[J]. Journal of Electronics & Information Technology, 2018, 40(6): 1360-1367. doi: 10.11999/JEIT170800

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Online Sequential Extreme Learning Machine Based on M-estimator and Variable Forgetting Factor

doi: 10.11999/JEIT170800

1.
2.
(College of Information Engineering, Yancheng Teachers University, Yancheng 224002, China)

Funds:

The National Natural Science Foundation of China (61603326, 61379064, 61273106)

Received Date: 2017-08-08
Rev Recd Date: 2018-01-16
Publish Date: 2018-06-19

Abstract

Abstract

To solve the online learning problem under the scenario of time-varying and containing outliers, this paper proposes an M-estimator and Variable Forgetting Factor based Online Sequential Extreme Learning Machine (VFF-M-OSELM). The VFF-M-OSELM is developed from the online sequential extreme learning machine algorithm and retains the same excellent sequential learning ability as it, it replaces the conventional Least-Squares (LS) cost function with a robust M-estimator based cost function to enhance the robustness of the learning model to outliers. Meanwhile, a new variable forgetting factor method is designed and incorporated in the VFF-M- OSELM to enhance further the dynamic tracking ability and adaptivity of the algorithm to time-varying system. The simulation results verify the effectiveness of the proposed algorithm.
- Online Sequential Extreme Learning Machine (OSELM),
- M-estimator,
- Variable Forgetting Factor (VFF),
- Robustness,
- Adaptivity

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References(20)

References

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