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An enhanced extreme learning machine based on ridge regression for regression

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Abstract

The extreme learning machine (ELM) is a novel single hidden layer feedforward neural network, which has the superiority in many aspects, especially in the training speed; however, there are still some shortages that restrict the further development of ELM, such as the perturbation and multicollinearity in the linear model. To the adverse effects caused by the perturbation or the multicollinearity, this paper proposes an enhanced ELM based on ridge regression (RR-ELM) for regression, which replaces the least square method to calculate output weights. With an additional adjustment of ridge regression, all the characteristics become even better. Simulative results show that the RR-ELM, compared with ELM, has better stability and generalization performance.

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Acknowledgments

This project is supported by the National Natural Science Foundation of China (Grant No. 60774028) and Natural Science Foundation of Hebei Province, China (Grant No. F2010001318).

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Authors and Affiliations

  1. Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, 066004, China

    Guoqiang Li & Peifeng Niu

  2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Qinhuangdao, 066004, China

    Guoqiang Li & Peifeng Niu

Authors
  1. Guoqiang Li
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  2. Peifeng Niu
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Correspondence to Guoqiang Li or Peifeng Niu.

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Li, G., Niu, P. An enhanced extreme learning machine based on ridge regression for regression. Neural Comput & Applic 22, 803–810 (2013). https://doi.org/10.1007/s00521-011-0771-7

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  • DOI: https://doi.org/10.1007/s00521-011-0771-7

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