EXTENDED NEO-FUZZY NEURON IN THE TASK OF IMAGES FILTERING

Authors

  • Ye. V. Bodyanskiy Kharkiv National University of Radio Electronics, Ukraine, Ukraine
  • N. E. Kulishova Kharkiv National University of Radio Electronics, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2014-1-16

Keywords:

color images, disturbance, contours, filtering, neo-fuzzy neuron.

Abstract

The paper describes a modification of the neo-fuzzy neuron called as «extended neo-fuzzy neuron» (ENFN) that characterized by improved approximating properties. The adaptive learning algorithm for ENFN is proposed, that has both following and smoothing properties and allows to solve problems of prediction, filtering and smoothing of non-stationary disturbed stochastic and chaotic signals. A distinctive feature of ENFN is its implementation computational simplicity compared with artificial neural networks and neuro-fuzzy systems. These properties of the proposed neo-fuzzy neuron make it very effective in suppressing noise in image filtering.

References

Rutkowski, L. Computational Intelligence. Methods and Techniques / Rutkowski L. – Berlin : Springer-Verlag, 2008. – 514 p.

Mumford, C. L. Computational Intelligence / C. L. Mumford, L. C. Jain. – Berlin : Springer-Verlag, 2009. – 725 p.

Kruse, R. Computational Intelligence. A Methodological Introduction / [Kruse R., Borgelt C., Klawonn F., Moewes C., Steinbrecher M., Held P.]. – Berlin : Springer-Verlag, 2013. – 488 p.

Du, K.-L. Neural Networks and Statistical Learning / K.-L. Du, M. N. S. Swamy. – London : Springer-Verlag, 2014. – 815 p.

Yamakawa, J. A neo-fuzzy neuron and its application to system identification and prediction of the system behavior / J. Yamakawa, E. Uchino, J. Miki, H. Kusanagi // Proc. 2-nd Int. Conf. on Fuzzy Logic and Neural Networks «IIZUKA-92», Iizuka, Japan, 1992. – P. 477–483.

Uchino, E. Soft computing based signal prediction, restoration and filtering / E. Uchino, J. Yamakawa ; Ed. Da Ruan «Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks and Genetic Algoritms». – Boston : Kluwer Academic Publishers, 1997. – P. 331–349.

Miki, J. Analog implementation of neo-fuzzy neuron and its on-board learning / J. Miki, J. Yamakawa ; Ed. by N. E. Mastorakis «Computational Intelligence and Applications». – Piraeus : WSES Press, 1999. – P. 144–149.

Takagi T. Fuzzy identification of systems and its application to modeling and control / T. Takagi, M. Sugeno // IEEE Trans. on System, Man and Cybernetics. – 1985. – vol. 15, no. 1. – P. 116–132

Published

2014-04-14

How to Cite

Bodyanskiy, Y. V., & Kulishova, N. E. (2014). EXTENDED NEO-FUZZY NEURON IN THE TASK OF IMAGES FILTERING. Radio Electronics, Computer Science, Control, (1). https://doi.org/10.15588/1607-3274-2014-1-16

Issue

Section

Neuroinformatics and intelligent systems

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