基于M-estimator與可變遺忘因子的在線貫序超限學(xué)習(xí)機
doi: 10.11999/JEIT170800
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2.
(鹽城師范學(xué)院信息工程學(xué)院 鹽城 224002) ②(南京航空航天大學(xué)計算機科學(xué)與技術(shù)學(xué)院 南京 210016)
基金項目:
國家自然科學(xué)基金(61603326, 61379064, 61273106)
Online Sequential Extreme Learning Machine Based on M-estimator and Variable Forgetting Factor
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2.
(College of Information Engineering, Yancheng Teachers University, Yancheng 224002, China)
Funds:
The National Natural Science Foundation of China (61603326, 61379064, 61273106)
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摘要: 該文針對時變離群值環(huán)境下的在線學(xué)習(xí)問題,提出一種基于M-estimator與可變遺忘因子的在線貫序超限學(xué)習(xí)機算法(VFF-M-OSELM)。VFF-M-OSELM以在線貫序超限學(xué)習(xí)機模型為基礎(chǔ),通過引入一種更加魯棒的M-estimator代價函數(shù)來替代傳統(tǒng)的最小二乘代價函數(shù),以提高模型對于離群值的在線處理能力和魯棒性。同時VFF-M-OSELM通過融合使用一種新的可變遺忘因子方法進(jìn)一步增強了其在時變環(huán)境下的動態(tài)跟蹤能力和自適應(yīng)性。仿真實例驗證了所提算法的有效性。
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關(guān)鍵詞:
- 在線貫序超限學(xué)習(xí)機 /
- M-估計 /
- 可變遺忘因子 /
- 魯棒性 /
- 自適應(yīng)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. -
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