SLFSNN BASED ON DISCRETE SECOND-ORDER CDRU FOR FUZZY CLUSTERING TASKS

Authors

  • Дар’я Михайлівна Малишева Харківський національний університет радіоелектроніки пр. Леніна, 14, м. Харків, Україна, 61166, Ukraine

DOI:

https://doi.org/10.15587/1729-4061.2012.4197

Keywords:

fuzzy clustering, spike, fuzzy spiking neural networks.

Abstract

The hybrid neural network based on the idea of combining spiking neural networks and the principles of fuzzy logic. The paper presents the architecture of self-learning fuzzy spiking neural network based on discrete second-order critically damped response units.

Author Biography

Дар’я Михайлівна Малишева, Харківський національний університет радіоелектроніки пр. Леніна, 14, м. Харків, Україна, 61166

Студентка

Кафедра штучного інтелекту

References

  1. Bodyanskiy, Ye. A self-learning spiking neural network for fuzzy clustering task. [Text] / Ye. Bodyanskiy, A. Dolotov // Scientific Journal of Riga Technical University: Information Technology and Management Science, 2008. – 36 – P. 27-33.
  2. Bohte, S.M. Unsupervised clustering with spiking neurons by sparse temporal coding and multi-layer RBF networks [Text] / S.M. Bohte, J.S. Kok J.S., H.La. Poutre // IEEE Trans on Neural Networks – 2002. – 13 – P. 426-435.
  3. Jang, J.-S.R. Neuro-Fuzzy and Soft Computing[Text] / J.-S.R. Jang, C.-T. Sun, E. Mizutani – Upper Saddle River: Prentice Hall, 1997. – 614 p.
  4. Natschlaeger, T. Spatial and temporal pattern analysis via spiking neurons. Network: Computations in Neural Systems [Text] / T. Natschlaeger, B. Ruf – 1998 – 9. – P. 319-332.

Published

2012-06-01

How to Cite

Малишева, Д. М. (2012). SLFSNN BASED ON DISCRETE SECOND-ORDER CDRU FOR FUZZY CLUSTERING TASKS. Eastern-European Journal of Enterprise Technologies, 3(11(57), 28–30. https://doi.org/10.15587/1729-4061.2012.4197