The compressed data predictor based on neuro-neo-fuzzy counterpropagation network

Authors

  • Ирина Павловна Плисс Kharkiv National University of Radio Electronics Lenina 14, Kharkov, 61166, Ukraine
  • Алексей Константинович Тищенко Kharkiv National University of Radio Electronics Lenina 14, Kharkov, 61166, Ukraine
  • Наталья Александровна Тесленко Kharkiv National University of Radio Electronics Lenina 14, Kharkov, 61166, Ukraine

DOI:

https://doi.org/10.15587/1729-4061.2012.3993

Keywords:

Compression, prediction, perceptron, neo-fuzzy-neuron

Abstract

The task of compressed nonlinear time series prediction with nonstationary characteristics is solved.  The new architecture of a neural network is proposed that consists of a three-layer “bottle-neck” perceptron and a counterpropagation neo-neuro-fuzzy network

Author Biographies

Ирина Павловна Плисс, Kharkiv National University of Radio Electronics Lenina 14, Kharkov, 61166

Ph.D., Senior Research Fellow, Senior Research Fellow

Problem research laboratory of automatic control systems

Алексей Константинович Тищенко, Kharkiv National University of Radio Electronics Lenina 14, Kharkov, 61166

Graduate student

Problem research laboratory of automatic control systems

Наталья Александровна Тесленко, Kharkiv National University of Radio Electronics Lenina 14, Kharkov, 61166

Ph.D., Senior Research Fellow

Problem research laboratory of automatic control systems

References

  1. Haykin, S. Neural Networks. A Comprehensive Foundation [Текст] / S. Haykin. – N.J.: Upper Saddle River, Prentice Hall, Inc., 1999. – 842 p.
  2. Arbib, M.A. The Handbook of Brain Theory and Neural Networks [Текст] / M.A. Arbib. – Madison: Impressions Books and Journals Services, Inc., 2003.
  3. – 1300p.
  4. Hristev, R.M. The ANN Book [Текст] / R.M. Hristev. – GNU Public Licence, 1998. – 392 p.
  5. Бодянский, Е.В. Прогнозирующая нейронная метасеть с нетрадиционными функциями активации [Текст] / Е.В. Бодянский, А.Н. Слипченко, Н.А. Тесленко // Автомобильный транспорт. – 2003. – 13. – С.273-275.
  6. Kohonen, T. Self-Organizing Maps [Текст] / Т. Kohonen. – Berlin: Springer-Verlag, 1995. – 362 p.
  7. Tsao, E. C.-K. Fuzzy Kohonen clustering networks [Текст] / E. C.-K. Tsao, J.C. Bezdek, N. R. Pal. // Pattern Recognition. – 1994. – V.5. – 27. – P. 757–764.
  8. Looney, C.G. A fuzzy clustering and fuzzy merging algorithm [Текст] / C.G. Looney. – Reno, NV, 1999.
  9. Bezdek, J.C. Pattern Recognition with Fuzzy Objective Function Algorithms [Текст] / J.C. Bezdek. – N.Y.: Plenum Press, 1981. – 272 p.
  10. Bodyanskiy, Ye. Recursive fuzzy clustering algorithms for segmentation of biological time series [Текст] / Ye. Bodyanskiy, Ye. Gorshkov, V. Kolodyazhniy, O. Shylo // Proc. 13-th East West Fuzzy Coll. 2006. – Zittau/Goerlitz: University of Applied Sciences (FH), 2006. – P. 130–139.
  11. Эйкхофф, П. Основы идентификации систем управления [Текст] / П. Эйкхофф. – М.: Мир, 1975. – 683с.
  12. Hecht-Nielsen, R. Counterpropagation Networks [Текст] / R. Hecht-Nielsen // Applied Optics. – 1987. – V. 26. – P. 4979–4984.
  13. Hecht-Nielsen, R. Applications of counterpropagation networks [Текст] / R. Hecht-Nielsen // Neural Networks. – 1988. – V.1. - 2. - P. 131–139.
  14. Zhang, Z. Fuzzy generalization of the counter-propagation neural network: a family of soft competitive basis function neural networks [Текст] / Z. Zhang, N. Zheng, T. Wang // Soft Computing. – 2001. – V.5. - 6. – P. 440–450.
  15. Horio, K. Modified counterpropagation employing neo fuzzy neurons and its application to system modelling [Текст] / K. Horio, T. Yamakawa // Proc. Int. Conf. on Info-tech and Info-net (ICII 2001). – IEEE Press, 2001. – V.4. – P. 50–55.
  16. Yamakawa, T. A neo fuzzy neuron and its applications to system identification and prediction of the system behaviour [Текст] / T. Yamakawa, E. Uchino, T. Miki, H. Kusanagi // Proc. 2nd Int. Conf. on Fuzzy Logic and Neural Networks ``IIZUKA-92''. – Iizuka, Japan, 1992. – P. 477–483.
  17. Тесленко, Н.А. Алгоритм обучения автоассоциативной искусственной многослойной нейронной сети [Текст] / Н.А. Тесленко // Бионика интеллекта. – 2004. – №1(61). – С.103-106.

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Published

2012-06-01

How to Cite

Плисс, И. П., Тищенко, А. К., & Тесленко, Н. А. (2012). The compressed data predictor based on neuro-neo-fuzzy counterpropagation network. Eastern-European Journal of Enterprise Technologies, 3(3(57), 14–17. https://doi.org/10.15587/1729-4061.2012.3993

Issue

Vol. 3 No. 3(57) (2012): Control systems

Section

Control systems

License

Copyright (c) 2014 Ирина Павловна Плисс, Алексей Константинович Тищенко, Наталья Александровна Тесленко

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