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: 1695
- HTML全文浏览量: 182
- 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

HTML全文

参考文献(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, 徐涛, 汤克明, 等. 具有广义正则化与遗忘机制的在线贯序超限学习机[J]. 控制与决策, 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.

施引文献

资源附件(0)

访问统计