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A Dynamic ELM with Balanced Variance and Bias for Long-Term Online Prediction

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Abstract

For long-term online prediction of nonlinear time series, how to determine a feasible network architecture that conforms to the time-varying data stream is recognized to be a challenging problem. To deal with the issue, a dynamic ELM with balanced variance and bias has been proposed. A suitable fitting degree, which contains model applicability at sequential learning phase, is taken into consideration. Based on the shifting error of the sequence fragment, the automatic model update strategy is exploited. Transformable parameters help reduce the overfitting and underfitting at the same time, and avoid the trial and error caused by user intervention effectively, so as to guarantee the feasibility for long-term online prediction. Furthermore, hidden node number and the regularization parameter can be calculated according to the fast-changing test data, thus building an optimum network architecture quantificationally. Experimental results verify that the proposed algorithm has better generalization performance on various long-term regression problems.

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Acknowledgements

The work was supported by the National Key Research Project of China under Grant No. 2016YFB1001304, the National Natural Science Foundation of China under Grant 61572229, the JLUSTIRT High-level Innovation Team, and the Fundamental Research Funds for Central Universities under Grant No. 2017TD-19. The authors gratefully acknowledge financial support from the Research Centre for Intelligent Signal Identification and Equipment, Jilin Province.

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Authors and Affiliations

  1. College of Communication Engineering, Jilin University, Changchun, 130022, Jilin, China

    Haiyang Yu & Xiaoying Sun

  2. College of Computer Science and Technology, Jilin University, Changchun, 130012, Jilin, China

    Jian Wang

  3. Department of Intelligent Vehicle, China Automotive Engineering Research Institute (CAERI), Chongqing, 404100, China

    Jian Wang

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  1. Haiyang Yu
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  2. Xiaoying Sun
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  3. Jian Wang
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Correspondence to Xiaoying Sun.

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Yu, H., Sun, X. & Wang, J. A Dynamic ELM with Balanced Variance and Bias for Long-Term Online Prediction. Neural Process Lett 49, 1257–1271 (2019). https://doi.org/10.1007/s11063-018-9865-x

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  • DOI: https://doi.org/10.1007/s11063-018-9865-x

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