AN EVOLVING CASCADE SYSTEM BASED ON NEURO-FUZZY NODES

Authors

  • Ye. V. Bodyanskiy Kharkiv National University of Radio Electronics, Kharkiv, Ukraine, Ukraine
  • O. K. Tyshchenko Kharkiv National University of Radio Electronics, Kharkiv, Ukraine, Ukraine
  • O. O. Boiko Kharkiv National University of Radio Electronics, Kharkiv, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2016-2-5

Keywords:

hybrid system, Computational Intelligence, cascade system, neuro-fuzzy system, membership function, evolving system.

Abstract

An evolving cascade system based on fuzzy-neurons and its learning procedures are proposed in the paper. During a learning procedure in
an online mode, the proposed system tunes both its parameters and its architecture. Neuro-fuzzy systems are proposed as nodes of the evolving cascade system. A method based on the gradient procedure of a learning criterion minimization is proposed for membership functions’ tuning in the neuro-fuzzy nodes. Synaptic weights, centers and width parameters of the membership functions are tuned during the learning procedure. Software that implements the proposed evolving cascade neuro-fuzzy system’s architecture has been developed. A number of experiments has been held in order to research the proposed system’s properties. Experimental results have proven the fact that the proposed system could be used to solve a wide range of Data Mining tasks. Data sets are processed in an online mode. The proposed system provides computational simplicity, and data sets are processed faster due to the possibility of parallel tuning for the evolving cascade system. A distinguishing feature of the proposed system is that there is no need of a large training set for the system to be tuned.

References

Bifet A. Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams / A. Bifet. – IOS Press, 2010. – 224 p. 2. Kasabov N. Evolving fuzzy neural networks : theory and applications for on-line adaptive prediction, decision making and control / N. Kasabov // Australian Journal of Intelligent Information Processing Systems. – 1998. – Vol. 5, Issue 3. – P. 154–160. 3. Kasabov N. Evolving fuzzy neural networks for on-line supervised/unsupervised, knowledge-based learning / N. Kasabov // IEEE Transactions on Man, Machine, and Cybernetics. – 2001. – Vol. 31, Issue 6. – P. 902–918. 4. Kasabov N. Evolving Connectionist Systems / N. Kasabov. – London : Springer-Verlag, 2003. – 307 p. 5. Lughofer E. Evolving Fuzzy Systems – Methodologies, Advanced Concepts and Applications / E. Lughofer. – Berlin : Springer, 2011. – 410 p. 6. Ivakhnenko A. G. Polynomial theory of complex systems / A. G. Ivakhnenko // IEEE Transactions on Systems, Man, and Cybernetics. – 1971. – Vol. 1, Issue 4. – P. 364–378. 7. Ивахненко А. Г. Самообучающиеся системы распознавания и автоматического управления / А. Г. Ивахненко. – Киев : Техніка, 1969. – 392 с. 8. Ивахненко А. Г. Долгосрочное прогнозирование и управление сложными системами / А. Г. Ивахненко. – Киев : Техніка, 1975. – 311 с. 9. Ивахненко А. Г. Помехоустойчивость моделирования / А. Г. Ивахненко, В. С. Степашко. – Киев : Наукова думка, 1985. – 216 с.

Downloads

How to Cite

Bodyanskiy, Y. V., Tyshchenko, O. K., & Boiko, O. O. (2016). AN EVOLVING CASCADE SYSTEM BASED ON NEURO-FUZZY NODES. Radio Electronics, Computer Science, Control, (2). https://doi.org/10.15588/1607-3274-2016-2-5

Issue

No. 2 (2016): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

License

Copyright (c) 2016 Ye. V. Bodyanskiy, O. K. Tyshchenko, O. O. Boiko

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons Licensing Notifications in the Copyright Notices

The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.

The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.

The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.