基于M-estimator與可變遺忘因子的在線貫序超限學(xué)習(xí)機

郭威; 徐濤; 于建江; 湯克明

doi:10.11999/JEIT170800

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

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)

計量
- 文章訪問數(shù): 1724
- HTML全文瀏覽量: 191
- PDF下載量: 124
- 被引次數(shù): 0
出版歷程
- 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: 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。
- 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.
- Online Sequential Extreme Learning Machine (OSELM) /
- M-estimator /
- Variable Forgetting Factor (VFF) /
- Robustness /
- Adaptivity

HTML全文

參考文獻(xiàn)(20)

LUGHOFER E. On-line active learning: A new paradigm to improve practical useability of data stream modeling methods[J]. Information Sciences, 2017, 415(1): 356-376. doi: 10.1016/j.ins.2017.06.038.

ZHANG Q, ZHANG P, LONG G, et al. Online learning from trapezoidal data streams[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(10): 2709-2723. doi: 10.1109/TKDE.2016.2563424.

LIANG N Y, HUANG G B, SARATCHANDRAN P, et al. A fast and accurate online sequential learning algorithm for feedforward networks[J]. IEEE Transactions on Neural Networks, 2006, 17(6): 1411-1423. doi: 10.1109/TNN.2006. 880583.

HUANG G B, ZHU Q Y, and SIEW C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1): 489-501. doi: 10.1016/j.neucom.2005.12.126.

LU X, ZHOU C, HUANG M, et al. Regularized online sequential extreme learning machine with adaptive regulation factor for time-varying nonlinear system[J]. Neurocomputing, 2016, 174(1): 617-626. doi: 10.1016/j.neucom.2015.09.068.

WANG X and HAN M. Online sequential extreme learning machine with kernels for nonstationary time series prediction [J]. Neurocomputing, 2014, 145(12): 90-97. doi: 10.1016/ j.neucom.2014.05.068.

WANG X and HAN M. Improved extreme learning machine for multivariate time series online sequential prediction[J]. Engineering Applications of Artificial Intelligence, 2015, 40(4): 28-36. doi: 10.1016/j.engappai.2014.12.013.

HUYNH H T and WON Y. Regularized online sequential learning algorithm for single-hidden layer feedforward neural networks[J]. Pattern Recognition Letters, 2011, 32(14): 1930-1935. doi: 10.1016/j.patrec.2011.07.016.

SUN L, CHEN B, TOH K A, et al. Sequential extreme learning machine incorporating survival error potential[J]. Neurocomputing, 2015, 155(5): 194-204. doi: 10.1016/j. neucom.2014.12.029.

SUN J, FUJITA H, CHEN P, et al. Dynamic financial distress prediction with concept drift based on time weighting combined with Adaboost support vector machine ensemble[J]. Knowledge-Based Systems, 2017, 120(C): 4-14. doi: 10.1016/ j.knosys.2016.12.019.

郭威, 徐濤, 湯克明, 等. 具有廣義正則化與遺忘機制的在線貫序超限學(xué)習(xí)機[J]. 控制與決策, 2017, 32(2): 247-254. doi: 10.13195/j.kzyjc.2015.1385.

GUO Wei, XU Tao, TANG Keming, et al. Online sequential extreme learning machine with generalized regularization and forgetting mechanism[J]. Control and Decision, 2017, 32(2): 247-254. doi: 10.13195/j.kzyjc.2015.1385.

CELAYA E and AGOSTINI A. Online EM with weight-based forgetting[J]. Neural Computation, 2015, 27(5): 1142-1157. doi: 10.1162/NECO_a_00723.

LIM J, LEE S, and PANG H. Low complexity adaptive forgetting factor for online sequential extreme learning machine (OS-ELM) for application to nonstationary system estimations[J]. Neural Computing and Applications, 2013, 22(3-4): 569-576. doi: 10.1007/s00521-012-0873-x.

SOARES S G and ARAUJO R. An adaptive ensemble of on-line Extreme Learning Machines with variable forgetting factor for dynamic system prediction[J]. Neurocomputing, 2016, 171(C): 693-707. doi: 10.1016/j.neucom.2015.07.035.

GOLUB G H and VAN LOAN C F. Matrix Computations[M]. Baltimore: JHU Press, 2012: 65.

BARRETO G A and BARROS A. A robust extreme learning machine for pattern classification with outliers[J]. Neurocomputing, 2016, 176(C): 3-13. doi: 10.1016/j.neucom. 2014.10.095.

ROUSSEEUW P J and LEROY A M. Robust Regression and Outlier Detection[M]. New York: John Wiley Sons, 2005: 43-44.

PEREZ-SANCHEZ B, FEONTENLA-ROMERO O, GUIJARRO-BERDINAS B, et al. An online learning algorithm for adaptable topologies of neural networks[J]. Expert Systems with Applications, 2013, 40(18): 7294-7304. doi: 10.1016/j.eswa.2013.06.066.

ZHOU X, LIU Z, and ZHU C. Online regularized and kernelized extreme learning machines with forgetting mechanism[J]. Mathematical Problems in Engineering, 2014, 2014(1): 1-11. doi: 10.1155/2014/938548.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問統(tǒng)計