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所属单位：能源与动力学院

发表刊物：APPLIED SCIENCES-BASEL

关键字：time series prediction extreme learning machine adaptive weight online learning

摘要：A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predicting time series problems based on an online sequential extreme learning machine (OS-ELM) in this paper. In real-world online applications, the sequentially coming data chunk usually possesses varying confidence coefficients, and the data chunk with a low confidence coefficient tends to mislead the subsequent training process. The proposed AWOS-ELM can improve the training process by accessing the confidence coefficient adaptively and determining the training weight accordingly. Experiments on six time series prediction data sets have verified that the AWOS-ELM algorithm performs better in generalization performance, stability, and prediction ability than the OS-ELM algorithm. In addition, a real-world mechanical system identification problem is considered to test the feasibility and efficacy of the AWOS-ELM algorithm.

ISSN号：2076-3417

是否译文：否

发表时间：2017-03-01

合写作者：Lu, Junjie,鲁峰

通讯作者：黄金泉