Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Information Technology and Management Science

The Journal of Riga Technical University

1 Issue per year

Open Access
Online
ISSN
2255-9094
See all formats and pricing
More options …

Flexible Neo-fuzzy Neuron and Neuro-fuzzy Network for Monitoring Time Series Properties

Yevgeniy Bodyanskiy / Iryna Pliss / Olena Vynokurova
Published Online: 2014-01-25 | DOI: https://doi.org/10.2478/itms-2013-0007

Abstract

In the paper, a new flexible modification of neofuzzy neuron, neuro-fuzzy network based on these neurons and adaptive learning algorithms for the tuning of their all parameters are proposed. The algorithms are of interest because they ensure the on-line tuning of not only the synaptic weights and membership function parameters but also forms of these functions that provide improving approximation properties and allow avoiding the occurrence of “gaps” in the space of inputs.

Keywords : Flexible activation-membership function; flexible neo-fuzzy neuron; forecasting; identification learning algorithm

  • [1] V. Raghavan, A. Hafez “Dynamic Data Mining”, J. of the American Society for Information Science, pp. 220-229, 2000.Google Scholar

  • [2] E. Lughofer, Evolving Fuzzy Systems: Methodologies, Advanced Concepts and Applications. Springer, 2011.Google Scholar

  • [3] R.H. Abiyev, O. Kaynak “Fuzzy wavelet neural networks for identification and control of dynamic plants - A novel structure and a comparative study”, IEEE Trans. on Industrial Electronics, vol. 55(8), pp. 3133-3140, 2008.Web of ScienceGoogle Scholar

  • [4] Ye. Bodyanskiy, I. Pliss, O. Vynokurova, “Adaptive wavelet-neurofuzzy network in the forecasting and emulation tasks”, Int. J. on Information Theory and Applications, vol. 15(1), pp. 47-55, 2008.Google Scholar

  • [5] Ye. Bodyanskiy, I. Pliss, O. Vynokurova, “Hybrid wavelet-neuro-fuzzy system using adaptive W-neurons”, Wissenschaftliche Berichte, FH Zittau/Goerlitz, vol. 106(N.2454-2490), pp. 301-308, 2008.Google Scholar

  • [6] Ye. Bodyanskiy and I. Pliss and O. Vynokurova Hybrid GMDH-neural network of computational intelligence. Proc. 3rd International Workshop on Inductive Modelling, Poland, Krynica, 2009.Google Scholar

  • [7] T. Miki and T. Yamakawa, “Analog implementation of neo-fuzzy neuron and its on board learning”. in Computational Intelligence and Application, N.E. Mastorakis, Ed., WSES Press, 1999, pp. 144-149.Google Scholar

  • [8] T. Yamakawa and T. Miki and E. Uchino and H. Kusanagi A neo fuzzy neuron and its applications to system identification and prediction of the system behaviour. Proc. 2-nd Int. Conf. on Fuzzy Logic and Neural Networks - "IIZUKA-92", Iizuka, Japan, 1992.Google Scholar

  • [9] E. Uchino and T. Yamakawa, “Soft computing bases signal prediction, restoration, and filtering”, in Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms, Da Ruan, Ed., Boston, Kluwer Academic Publishers, 1997, pp. 331-349.Google Scholar

  • [10] Ye. Bodyanskiy and I. Kokshenev and V. Kolodyazhniy An adaptive learning algorithm for a neo fuzzy neuron. Proc. 3-nd Int. Conf. of European Union Society for Fuzzy Logic and Technology (EUSFLAT'03), Zittau, Germany, 2003.Google Scholar

  • [11] G.C. Goodwin, P.J. Ramadge, P.E. Caines “A globally convergent adaptive predictor”. Automatica, vol. 17(1), pp. 135-140, 1981.CrossrefGoogle Scholar

  • [12] Ye.V. Gorshkov, V.V. Kolodyazhniy, I.P. Pliss “Adaptive learning algorithm for a neo-fuzzy neuron and neuro-fuzzy network based on a polynomial membership functions”, Bionica Intellecta: Sci. Mag., vol. 61(1), pp. 78-81, 2004.Google Scholar

  • [13] Ye. Bodyanskiy, Ye. Viktorov, “The cascade neo-fuzzy architecture using cubic-spline activation functions”, Int. J. Information Theories and Application, vol. 16(3), pp. 245-259, 2009.Google Scholar

  • [14] V. Kolodyazhniy, Ye. Bodyanskiy, Cascaded multiresolution splinebased fuzzy neural network. Eds. P. Angelov, D. Filev, N.Kasabov. Proc. Int. Symp. on Evolving Intelligent Systems, Leicester, UK, 2010.Google Scholar

  • [15] Ye. Bodyanskiy, N. Lamonova, I. Pliss, O. Vynokurova, “An adaptive learning algorithm for a wavelet neural network”, Expert Systems, vol. 22(5), pp. 235-240, 2005.Google Scholar

  • [16] Ye. Bodyanskiy, O. Vynokurova, “Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification”, Information Science, vol. 220, pp. 170-179, 2013.Google Scholar

  • [17] L.X. Wang, J. Mendel, “Generating fuzzy rules by learning from examples” IEEE Trans. Syst., Man, and Cyb., vol. 22, pp. 1414-1427, 1992.Google Scholar

  • [18] J.-S. Jang, C.-T. Sun, and E. Mizutani Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, 1997.Google Scholar

About the article

Yevgeniy Bodyanskiy

Yevgeniy Bodyanskiy graduated with distinction from Kharkiv National University of Radio Electronics (KhNURE) in 1971. In 1980 he defended the candidate thesis. In 1984 he was awarded the academic title of Senior Researcher. In 1990 he defended the doctor thesis (Dr.habil.sc.ing.). In 1994 he was awarded the academic title of Professor. His major fields of research are technical cybernetics and information theory, control and technical systems. Since 1974 he has been working at Kharkiv National University of Radio Electronics. In 1974–1976 he was a Researcher; in 1977–1983 – Senior Researcher; 1986–1991 – Scientific Head of Control Systems Research Laboratory; 1991–1992 – Fellow Researcher; since 1992 he has been a Professor of Artificial Intelligence Department at KhNURE, Scientific Head of Control Systems Research Laboratory at KhNURE. He has more than 500 scientific publications, including 40 inventions and 10 monographs. Research interests include hybrid systems of computational intelligence: adaptive, neuro-, wavelet-, neo-fuzzy-, real-time systems, including problems connected with control, identification, forecasting, clustering, diagnostics, fault detection in technical, economical, medical and ecological objects. He is a senior member of IEEE, member of 4 scientific and 7 editorial boards. Contact data: Office 511, Lenin av., 14, Kharkiv, 61166, Ukraine

Iryna Pliss

Iryna Pliss received her Qualification of Electrical Engineer from Kharkiv National University of Radio Electronics, Ukraine in 1970. In 1973–1976 she was a postgraduate student at the Artificial Intelligence Department. In 1979 she defended the candidate thesis. In 1984 she was awarded the academic title of Senior Researcher. Her major field of research is neuro-fuzzy systems of computational intelligence. At present, she is a Leading Researcher at the Control Systems Research Laboratory, Kharkiv National University of Radio Electronics. She is a member of the IEEE Signal Processing Society and the Neural Network Society. She has more than 150 publications and five inventions. Research interests include computational intelligence, data mining: fuzzy clustering algorithms based on neuro-fuzzy models. Contact data: Office 511, Lenin av., 14, Kharkiv, 61166, Ukraine

Olena Vynokurova

Olena Vynokurova graduated with distinction from Kharkiv National University of Radio Electronics in 2002. In 2002–2005 she was a postgraduate student at the Artificial Intelligence Department. In 2005 she defended the candidate thesis. In 2007 she was awarded the academic title of Senior Researcher. In 2012 she defended the doctoral thesis (Dr.habil.sc.ing.). Her major field of research is hybrid neuro-fuzzy systems used for dynamic data mining. Since 2002 she has been working at Kharkiv National University of Radio Electronics. In 2002–2005 she was a Researcher; in 2005–2010 – Senior Researcher; since 2010 she has been a Leading Researcher of Control Systems Research Laboratory; since 2013 she has been a Professor of the Department of Information Technology Security at KhNURE. She has more than 100 scientific publications, including 2 monographs. Research interests include the development of hybrid systems of computational intelligence: wavelet neural networks, hybrid wavelet neuro-fuzzy systems, identification, forecasting, clustering, diagnostics, fault detection in technical, economical, medical and ecological objects. Contact data: Office 517, Lenin av., 14, Kharkiv, 61166, Ukraine.


Published Online: 2014-01-25

Published in Print: 2013-12-01


Citation Information: Information Technology and Management Science, Volume 16, Issue 1, Pages 47–52, ISSN (Online) 2255-9094, ISSN (Print) 2255-9086, DOI: https://doi.org/10.2478/itms-2013-0007.

Export Citation

This content is open access.

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Daniel Zurita, Miguel Delgado, Jesus A. Carino, Juan A. Ortega, and Guy Clerc
IEEE Access, 2016, Volume 4, Page 6151
[2]
Ye. V. Bodyanskiy, O. O. Boiko, and I. P. Pliss
Cybernetics and Systems Analysis, 2015, Volume 51, Number 4, Page 500

Comments (0)

Please log in or register to comment.
Log in