一种基于增量加权平均的在线序贯极限学习机算法
发布时间：2019-01-28 07:45
【摘要】：针对在线序贯极限学习机(OS-ELM)对增量数据学习效率低、准确性差的问题,提出一种基于增量加权平均的在线序贯极限学习机(WOS-ELM)算法.将算法的原始数据训练模型残差与增量数据训练模型残差进行加权作为代价函数,推导出用于均衡原始数据与增量数据的训练模型,利用原始数据来弱化增量数据的波动,使在线极限学习机具有较好的稳定性,从而提高算法的学习效率和准确性.仿真实验结果表明,所提出的WOS-ELM算法对增量数据具有较好的预测精度和泛化能力.
[Abstract]:In order to solve the problem of low efficiency and poor accuracy of online sequential extreme learning machine (OS-ELM) for incremental data, an on-line sequential extreme learning machine (WOS-ELM) algorithm based on incremental weighted average is proposed. The residual of the original data training model and the residual of the incremental data training model are weighted as the cost function, and the training model used to balance the original data and the incremental data is deduced, and the fluctuation of the increment data is weakened by the original data. It can improve the learning efficiency and accuracy of the algorithm by making the online extreme learning machine more stable. Simulation results show that the proposed WOS-ELM algorithm has good prediction accuracy and generalization ability for incremental data.
【作者单位】： 东北大学计算机科学与工程学院;东软公司软件架构新技术国家重点实验室;
【基金】：国家863计划项目(2015AA016005) 国家自然科学基金项目(61402096,61173153,61300196)
【分类号】：TP18
本文编号：2416850
[Abstract]:In order to solve the problem of low efficiency and poor accuracy of online sequential extreme learning machine (OS-ELM) for incremental data, an on-line sequential extreme learning machine (WOS-ELM) algorithm based on incremental weighted average is proposed. The residual of the original data training model and the residual of the incremental data training model are weighted as the cost function, and the training model used to balance the original data and the incremental data is deduced, and the fluctuation of the increment data is weakened by the original data. It can improve the learning efficiency and accuracy of the algorithm by making the online extreme learning machine more stable. Simulation results show that the proposed WOS-ELM algorithm has good prediction accuracy and generalization ability for incremental data.
【作者单位】： 东北大学计算机科学与工程学院;东软公司软件架构新技术国家重点实验室;
【基金】：国家863计划项目(2015AA016005) 国家自然科学基金项目(61402096,61173153,61300196)
【分类号】：TP18
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