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Appl. Sci. 2017, 7(3), 217; doi:10.3390/app7030217

Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine

Jiangsu Province Key Laboratory of Aerospace Power Systems, College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
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Academic Editor: Chien-Hung Liu
Received: 5 January 2017 / Accepted: 20 February 2017 / Published: 2 March 2017
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Abstract

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. View Full-Text
Keywords: time series prediction; extreme learning machine; adaptive weight; online learning time series prediction; extreme learning machine; adaptive weight; online learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lu, J.; Huang, J.; Lu, F. Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine. Appl. Sci. 2017, 7, 217.

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