Skip to main content
SpringerLink
Log in

A reservoir radial-basis function neural network in prediction tasks

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

A reservoir radial-basis function neural network, which is based on the ideas of reservoir computing and neural networks and designated for solving extrapolation tasks of nonlinear non-stationary stochastic and chaotic time series under conditions of a short learning sample, is proposed in the paper. The network is built with the help of a radial-basis function neural network with an input layer, which is organized in a special manner and a kernel membership function. The proposed system provides high approximation quality in terms of a mean squared error and a high convergence speed using the second-order learning procedure. A software product that implements the proposed neural network has been developed. A number of experiments have been held in order to research the system’s properties. Experimental results prove the fact that the developed architecture can be used in Data Mining tasks and the fact that the proposed neural network has a higher accuracy compared to traditional forecasting neural systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Jaeger, H., The Echo State Approach to Analyzing and Training Recurrent Neural Networks, Berlin: German National Research Center for Information Technology, 2001.

    Google Scholar 

  2. Maass, W., Legenstein, R.A., and Markram, H., A new approach towards vision suggested by biologically realistic neural microcircuit models, Proc. of the 2nd Workshop on Biologically Motivated Computer Vision, Lecture Notes in Computer Science, Berlin: Springer, 2002, pp. 124–132.

    Google Scholar 

  3. Jaeger, H., Short Term Memory in Echo State Networks, Berlin: German National Research Center for Information Technology, 2001.

    Google Scholar 

  4. Maass, W. and Natschläger, T., Fading memory and kernel properties of generic cortical microcircuit models, J. Physiol., Orlando: Academic Press, 2004, vol. 98, no. 4, pp. 315–330.

    Google Scholar 

  5. Jaeger, H., Reservoir riddles: Suggestions for echo state network research, Proceedings of the International Joint Conference on Neural Networks, New York: Springer-Verlag, 2005. pp. 1460–1462.

    Google Scholar 

  6. Jaeger, H., Adaptive nonlinear system identification with echo state networks, Adv. Neural Inf. Process. Syst., 2003, vol. 15, no. 3, pp. 593–600.

    Google Scholar 

  7. Steil, J.J., Online stability of backpropagation-decorrelation recurrent learning, Neurocomputing, 2006, vol. 69, no. 7, pp. 642–650.

    Article  Google Scholar 

  8. Wyffels, F., Schrauwen, B., and Stroobandt, D., Using reservoir computing in a decomposition approach for time series prediction, Proceedings of the European Symposium on Time Series Prediction, Amsterdam, 2008, pp. 149–158.

    Google Scholar 

  9. Park, J. and Sandberg, I.W., Universal approximation using radial-basis-function networks, Neural Comput., 1991, vol. 3, pp. 246–257.

    Article  Google Scholar 

  10. Sunil, E.V.T. and Yung, C.Sh., Radial basis function neural network for approximation and estimation of nonlinear stochastic dynamic systems, IEEE Trans. Neural Networks, 1994, vol. 5, pp. 594–603.

    Article  Google Scholar 

  11. Bishop, C.M., Neural Networks for Pattern Recognition, Oxford: Clarendon Press, 1995.

    MATH  Google Scholar 

  12. Paetz, J., Reducing the number of neurons in radial basis function networks with dynamic decay adjustment, Neurocomputing, 2004, vol. 62, pp. 79–91.

    Article  Google Scholar 

  13. Bodyanskiy, Ye.V. and Deineko, A.A., Adaptive learning of an architecture and RBFN’s parameters, Sist. Tekhnol., 2013, vol. 4, no. 87, pp. 166–173.

    Google Scholar 

  14. Bodyanskiy, Ye.V., Deineko, A.A., and Stolnikova, M.Z., Adaptive learning of all parameters in an evolving radial-basis function network, Sist. Obrobky Inf., 2013, vol. 5, no. 112, pp. 13–16.

    Google Scholar 

  15. Epanechnikov, V., Non-parametric estimation of multivariate probability density, Teor. Veroyatn. Ee Primen., 1968, vol. 14, no. 1, pp. 156–161.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Control Systems Research Laboratory Kharkiv National University of Radio Electronics, 14 Lenin Ave., Kharkiv, 61166, Ukraine

    Oleksii K. Tyshchenko

Authors
  1. Oleksii K. Tyshchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oleksii K. Tyshchenko.

Additional information

The article is published in the original.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tyshchenko, O.K. A reservoir radial-basis function neural network in prediction tasks. Aut. Control Comp. Sci. 50, 65–71 (2016). https://doi.org/10.3103/S0146411616020061

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411616020061

Keywords

Search

Navigation