Extreme Learning Machine as a Function Approximator: Initialization of Input Weights and Biases

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 403)

Abstract

Extreme learning machine is a new scheme for learning the feedforward neural network, where the input weights and biases determining the nonlinear feature mapping are initiated randomly and are not learned. In this work, we analyze approximation ability of the extreme learning machine depending on the activation function type and ranges from which input weights and biases are randomly generated. The studies are performed on the example of approximation of one variable function with varying complexity. The ranges of input weights and biases are determined for ensuring the sufficient flexibility of the set of activation functions to approximate the target function in the input interval.

Keywords

Extreme learning machine Function approximation Activation functions Feedforward neural networks 

References

  1. 1.
    Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)CrossRefGoogle Scholar
  2. 2.
    Huang, G.-B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern.—Part B: Cybern. 42(2), 513–529 (2012)CrossRefGoogle Scholar
  3. 3.
    Huang, G., Huang, G.-B., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Netw. 61(1), 32–48 (2015)CrossRefMATHGoogle Scholar
  4. 4.
    Dudek, G.: Extreme learning machine for function approximation—interval problem of input weights and biases. In: Proceedings of the 2nd IEEE International Conference on Cybernetics (CYBCONF), pp. 62–67 (2015). http://dx.doi.org/10.1109/CYBConf.2015.7175907

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Electrical EngineeringCzestochowa University of TechnologyCzestochowaPoland

Personalised recommendations