| 具有广义正则化与遗忘机制的在线贯序超限学习机 |
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| 引用本文： | 郭威,徐涛,汤克明,于建江.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. |
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| 作者姓名： | 郭威 徐涛 汤克明 于建江 |
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| 作者单位： | 南京航空航天大学计算机科学与技术学院,南京210016;盐城师范学院信息工程学院,江苏盐城224002,南京航空航天大学计算机科学与技术学院,南京210016;中国民航大学计算机科学与 技术学院,天津300300,盐城师范学院信息工程学院,江苏盐城224002,盐城师范学院信息工程学院,江苏盐城224002 |
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| 基金项目： | 国家自然科学基金项目(61603326,61379064,61273106)；国家科技支撑计划课题(2014BAJ04B02)；中央高校基本科研业务费专项资金项目(3122014D032)；中国民航信息技术科研基地开放课题(CAAC-ITRB-201401). |
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| 摘 要： | 针对非平稳时间序列预测问题,提出一种具有广义正则化与遗忘机制的在线贯序超限学习机算法.该算法以增量学习新样本的方式实现在线学习,以遗忘旧的失效样本的方式增强对非平稳系统的动态跟踪能力,并通过引入一种广义的$l_2$正则化使其具有持续的正则化功能,从而保证算法的持续稳定性.仿真实例表明,所提出算法具有较同类算法更好的稳定性和更小的预测误差,适用于具有动态变化特性的非平稳时间序列在线建模与预测.
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| 关 键 词： | 在线贯序超限学习机 广义正则化 遗忘因子 时间序列预测 |
| 收稿时间： | 2015/11/10 0:00:00 |
| 修稿时间： | 2015/11/10 0:00:00 |
Online sequential extreme learning machine with generalized regularization and forgetting mechanism |
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| GUO Wei,XU Tao,TANG Ke-ming and YU Jian-jiang.Online sequential extreme learning machine with generalized regularization and forgetting mechanism[J].Control and Decision,2017,32(2):247-254. |
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| Authors: | GUO Wei XU Tao TANG Ke-ming and YU Jian-jiang |
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| Affiliation: | School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics, Nanjing210016, China;School of Information Engineering,Yancheng Teachers University,Yancheng224002,China,School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics, Nanjing210016, China;School of Computer Science and Technology,Civil Aviation University of China,Tianjin300300,China,School of Information Engineering,Yancheng Teachers University,Yancheng224002,China and School of Information Engineering,Yancheng Teachers University,Yancheng224002,China |
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| Abstract: | To solve the prediction problem of nonstationary time series, this paper proposes an online sequential extreme learning machine with forgetting and generalized regularization(OSELM-FGR). The proposed OSELM-FGR is able to learn the newly arrived samples incrementally by a recursive fashion, and has the improved ability to track the dynamic behavior of time-varying systems by forgetting the outdated samples in the learning process. Moreover, a generalized $l_2 $ regularization is introduced into the OSELM-FGR to make the proposed algorithm have a persistent stability. Detailed performance comparisons of the OSELM-FGR with its counterparts are carried out. The experimental results show that, the proposed OSELM-FGR has better performance in the sense of stability and prediction accuracy, which can be applied to the online modeling and prediction of nonstationary time series with dynamic changes. |
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