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| Online Sequential Extreme Learning Machine Based on M-estimator and Variable Forgetting Factor |
| GUO Wei①② XU Tao② YU Jianjiang① TANG Keming① |
①(College of Information Engineering, Yancheng Teachers University, Yancheng 224002, China)
②(College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China) |
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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.
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Received: 08 August 2017
Published: 15 March 2018
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| Fund:The National Natural Science Foundation of China (61603326, 61379064, 61273106) |
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Corresponding Authors:
GUO Wei
E-mail: weiguo031@163.com
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