基于M-estimator与可变遗忘因子的在线贯序超限学习机

郭威; 徐涛; 于建江; 汤克明

doi:10.11999/JEIT170800

基于M-estimator与可变遗忘因子的在线贯序超限学习机

doi: 10.11999/JEIT170800

2.
(盐城师范学院信息工程学院盐城 224002) ②(南京航空航天大学计算机科学与技术学院南京 210016)

基金项目:

国家自然科学基金(61603326, 61379064, 61273106)

计量
- 文章访问数: 1657
- HTML全文浏览量: 170
- PDF下载量: 123
- 被引次数: 0
出版历程
- 收稿日期: 2017-08-08
- 修回日期: 2018-01-16
- 刊出日期: 2018-06-19

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

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

Funds:

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

摘要

摘要: 该文针对时变离群值环境下的在线学习问题，提出一种基于M-estimator与可变遗忘因子的在线贯序超限学习机算法(VFF-M-OSELM)。VFF-M-OSELM以在线贯序超限学习机模型为基础，通过引入一种更加鲁棒的M-estimator代价函数来替代传统的最小二乘代价函数，以提高模型对于离群值的在线处理能力和鲁棒性。同时VFF-M-OSELM通过融合使用一种新的可变遗忘因子方法进一步增强了其在时变环境下的动态跟踪能力和自适应性。仿真实例验证了所提算法的有效性。
- 在线贯序超限学习机 /
- M-估计 /
- 可变遗忘因子 /
- 鲁棒性 /
- 自适应性
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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参考文献(20)

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