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Self-adaptive extreme learning machine

Abstract

In order to overcome the disadvantage of the traditional algorithm for SLFN (single-hidden layer feedforward neural network), an improved algorithm for SLFN, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem. In this paper, a self-adaptive mechanism is introduced into the ELM. Herein, a new variant of ELM, called self-adaptive extreme learning machine (SaELM), is proposed. SaELM is a self-adaptive learning algorithm that can always select the best neuron number in hidden layer to form the neural networks. There is no need to adjust any parameters in the training process. In order to prove the performance of the SaELM, it is used to solve the Italian wine and iris classification problems. Through the comparisons between SaELM and the traditional back propagation, basic ELM and general regression neural network, the results have proven that SaELM has a faster learning speed and better generalization performance when solving the classification problem.

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Acknowledgments

This work was supported by Research Fund for the Doctoral Program of Jiangsu Normal University (No. 13XLR041) and the National Natural Science Foundation of China (Nos. 61402207, 61100167, and 61272297), the Natural Science Foundation of Jiangsu Province, China, under Grant No. BK2011204, Qing Lan Project.

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Correspondence to Gai-Ge Wang.

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Cite this article

Wang, GG., Lu, M., Dong, YQ. et al. Self-adaptive extreme learning machine. Neural Comput & Applic 27, 291–303 (2016). https://doi.org/10.1007/s00521-015-1874-3

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Keywords

  • Classification
  • Self-adaptive
  • Extreme learning machine
  • Back propagation
  • General regression neural network