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首页> 外文期刊>Mathematical Problems in Engineering >Online Sequential Extreme Learning Machine with Generalized Regularization and Adaptive Forgetting Factor for Time-Varying System Prediction
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Online Sequential Extreme Learning Machine with Generalized Regularization and Adaptive Forgetting Factor for Time-Varying System Prediction

机译：具有广义正则化和自适应遗忘因子的在线时序极限学习机，用于时变系统预测

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摘要

Many real world applications are of time-varying nature and an online learning algorithm is preferred in tracking the realtime changes of the time-varying system. Online sequential extreme learning machine (OSELM) is an excellent online learning algorithm, and some improved OSELM algorithms incorporating forgetting mechanism have been developed to model and predict the time-varying system. But the existing algorithms suffer from a potential risk of instability due to the intrinsic ill-posed problem; besides, the adaptive tracking ability of these algorithms for complex time-varying system is still very weak. In order to overcome the above two problems, this paper proposes a novel OSELM algorithm with generalized regularization and adaptive forgetting factor (AFGR-OSELM). In the AFGR-OSELM, a new generalized regularization approach is employed to replace the traditional exponential forgetting regularization to make the algorithm have a constant regularization effect; consequently the potential ill-posed problem of the algorithm can be completely avoided and a persistent stability can be guaranteed. Moreover, the AFGR-OSELM adopts an adaptive scheme to adjust the forgetting factor dynamically and automatically in the online learning process so as to better track the dynamic changes of the time-varying system and reduce the adverse effects of the outdated data in time; thus it tends to provide desirable prediction results in time-varying environment. Detailed performance comparisons of AFGR-OSELM with other representative algorithms are carried out using artificial and real world data sets. The experimental results show that the proposed AFGR-OSELM has higher prediction accuracy with better stability than its counterparts for predicting time-varying system.
机译：许多现实世界的应用具有时变性质，因此在跟踪时变系统的实时变化时，首选在线学习算法。在线顺序极限学习机（OSELM）是一种出色的在线学习算法，并且已开发出一些具有遗忘机制的改进OSELM算法，以对时变系统进行建模和预测。但是，由于固有的不适定问题，现有算法存在潜在的不稳定风险。此外，这些算法对复杂时变系统的自适应跟踪能力仍然很弱。为了克服上述两个问题，本文提出了一种具有广义正则化和自适应遗忘因子（AFGR-OSELM）的新型OSELM算法。在AFGR-OSELM中，采用了一种新的广义正则化方法来代替传统的指数遗忘正则化，从而使算法具有恒定的正则化效果。因此，可以完全避免算法潜在的不适问题，并可以保证持久的稳定性。此外，AFGR-OSELM采用自适应方案，在在线学习过程中动态自动地调整遗忘因子，从而更好地跟踪时变系统的动态变化，及时减少过时数据的不良影响。因此，它倾向于在时变环境中提供理想的预测结果。 AFGR-OSELM与其他代表性算法的详细性能比较是使用人工和现实数据集进行的。实验结果表明，所提出的AFGR-OSELM比时变系统具有更高的预测精度和更好的稳定性。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第6期|6195387.1-6195387.22|共22页
  • 作者

    Guo Wei; Xu Tao; Tang Keming; Yu Jianjiang; Chen Shuangshuang;

  • 作者单位

    Yancheng Teachers Univ, Sch Informat Engn, Yancheng 224002, Peoples R China;

    Nanjing Univ Aeronaut & Astronaut, Sch Comp Sci & Technol, Nanjing 211106, Jiangsu, Peoples R China;

    Yancheng Teachers Univ, Sch Informat Engn, Yancheng 224002, Peoples R China;

    Yancheng Teachers Univ, Sch Informat Engn, Yancheng 224002, Peoples R China;

    Yancheng Teachers Univ, Sch Informat Engn, Yancheng 224002, Peoples R China;

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