Fast Stacking Neuro-Neo-Fuzzy System for Inverse Modeling in Online Mode

Authors

  • Yevgeniy Bodyanskiy
  • Oleh Zolotukhin
  • Andriy Yerokhin
  • Maryna Kudryavtseva
  • Maksym Yerokhin

Keywords:

artificial neural networks, artificial intelligence, computational intelligence, data visualization, inverse modeling, neuro-neo-fuzzy system, stacking hybrid systems, online adaptive learning, online ensemble learning, Python programming

Abstract

The article is devoted to the development of an approach based on hybrid systems of computational intelligence to solve Data Stream Mining tasks for complex technical objects. Such tasks arise during the identification, control and diagnostics of highly dynamic technical objects, for example, fiber-optic communication systems with rapidly growing traffic volumes. Often deep neural networks are used to solve such problems that require large amounts of training samples, a lot of time for their tuning and leads to the impossibility of effectively solving tasks in online mode with inverse modeling tasks being particularly complex. A new architecture of a stacking neuro-neo-fuzzy system is proposed that solves inverse modeling tasks, operates in online mode, has improved approximation capabilities, high speed and is characterized by simple computational implementation. The proposed stacking neuro-neo-fuzzy system is designed to solve the problem of inverse modeling of fiber optic communication systems in real time mode under conditions of limited training samples.

References

J. Kufel et al., “What is machine learning, artificial neural networks and deep learning?—Examples of practical applications in medicine,” Diagnostics, vol. 13, no. 15, Art. no. 2582, 2023. https://doi.org/10.3390/diagnostics13152582.

M. G. M. Abdolrasol et al., “Artificial neural networks based optimization techniques: A review,” Electronics, vol. 10, no. 21, Art. no. 2689, 2021, https://doi.org/10.3390/electronics10212689.

F. Aguirre et al., “Hardware implementation of memristor-based artificial neural networks,” Nature Communications, vol. 15, Art. no. 1974, 2024, https://doi.org/10.1038/s41467-024-45670-9.

A. Goel, A. K. Goel, and A. Kumar, “The role of artificial neural network and machine learning in utilizing spatial information,” Spatial Information Research, vol. 31, pp. 275–285, 2023, https://doi.org/10.1007/s41324-022-00494-x.

M. S. Roza Dastres and M. S. Soori, “Artificial neural network systems,” International Journal of Imaging and Robotics, vol. 21, no. 2, pp. 13–25, 2021.

L. Messeri and M. J. Crockett, “Artificial intelligence and illusions of understanding in scientific research,” Nature, vol. 627, pp. 49–58, 2024, https://doi.org/10.1038/s41586-024-07146-0.

S.-C. Vanegas-Ayala, J. Barón-Velandia, and E. Romero-Riaño, “Systematic review of forecasting models using evolving fuzzy systems,” Computation, vol. 12, no. 8, Art. no. 159, 2024, https://doi.org/10.3390/computation12080159.

M. E. Dogan, T. Goru Dogan, and A. Bozkurt, “The use of artificial intelligence (AI) in online learning and distance education processes: A systematic review of empirical studies,” Applied Sciences, vol. 13, no. 5, Art. no. 3056, 2023, https://doi.org/10.3390/app13053056.

S. Zhang et al., “Online adaptive CNN: A session-to-session transfer learning approach for non-stationary EEG,” Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, 2022, pp. 164–170, https://doi.org/10.1109/SSCI51031.2022.10022274.

I. Gligorea et al., “Adaptive learning using artificial intelligence in e-learning: A literature review,” Education Sciences, vol. 13, no. 12, Art. no. 1216, 2023, https://doi.org/10.3390/educsci13121216.

P. V. C. Souza, “Fuzzy neural networks and neuro-fuzzy networks: A review of the main techniques and applications used in the literature,” Applied Soft Computing, vol. 92, Art. no. 106275, 2020, https://doi.org/10.1016/j.asoc.2020.106275.

T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-15, no. 1, pp. 116–132, 1985. https://doi.org/10.1109/TSMC.1985.6313399.

J.-S. Jang, C.-N. Sun, and E. Mizutani, Neuro-Fuzzy and Soft Computing, New York, NY, USA: Prentice Hall, 1977.

E. Uchino and T. Yamakawa, “Soft computing based signal prediction, restoration, and filtering,” in Intelligent Hybrid Systems, D. Ruan, Ed. Boston, MA, USA: Springer, 1997, pp. 331–349, https://doi.org/10.1007/978-1-4615-6191-0_14.

T. Miki and T. Yamakawa, “Analog implementation of neo-fuzzy neuron and its on-board learning,” in Computational Intelligence and Applications, Piraeus, Greece: WSES Press, 1999, pp. 144–149.

I. Perfilieva, “Fuzzy transforms: Theory and applications,” Fuzzy Sets and Systems, vol. 157, no. 8, pp. 993–1023, 2006. https://doi.org/10.1016/j.fss.2005.11.012.

Ye. V. Bodyanskiy and N. O. Teslenko, “Adaptive F-transform using general regression neural networks,” Adaptive Systems of Automatic Control, no. 10 (30), pp. 25–31, 2007.

S. Popov, “Nonlinear visualization of incomplete data sets,” in Computer Science – Theory and Applications, D. Grigoriev, J. Harrison, and E. A. Hirsch, Eds. Berlin, Heidelberg: Springer, 2006, pp. 476–484, https://doi.org/10.1007/11753728_53.

V. A. Epanechnikov, “Non-parametric estimation of a multivariate probability density,” Theory of Probability and its Applications, vol. 14, no. 1, pp. 153–158, 1969. https://doi.org/10.1137/1114019.

Ye. Bodyanskiy, V. Kolodyazhniy, and A. Stephan, “An adaptive learning algorithm for a neuro-fuzzy network,” in Computational Intelligence: Theory and Application, B. Reusch, Ed. Berlin, Heidelberg, New York, NY, USA: Springer, 2001, pp. 68–75. https://doi.org/10.1007/3-540-45493-4_11.

S. Kaczmarz, “Approximate solution of systems of linear equations,” International Journal of Control, vol. 53, no. 6, pp. 1269–1271, 1993. https://doi.org/10.1080/00207179308934446.

V. Filatov, O. Zolotukhin, A. Yerokhin, and M. Kudryavtseva, “The methods for the prediction of climate control indicators in Internet of Things systems,” in CEUR Workshop Proceedings, 2021, pp. 391–400, https://doi.org/10.5281/zenodo.14526027.

S. Popov, “Large-scale data visualization with missing values,” Technological and Economic Development of Economy, vol. 12, no. 1, pp. 44–49, 2006, https://doi.org/10.3846/13928619.2006.9637721.

J.-S. R. Jang, “ANFIS: Adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993, https://doi.org/10.1109/21.256541.

A. Abraham, “Neuro-fuzzy systems: State-of-the-art modeling techniques,” in Computational Intelligence, IntechOpen, 2004, pp. 1–24, https://doi.org/10.5772/5555.

S. Vieira, U. Kaymak, and J. M. C. Sousa, “Fuzzy neural networks and neuro-fuzzy networks: A review of the main techniques and applications used in the literature,” Applied Soft Computing, vol. 92, art. no. 106275, 2020, https://doi.org/10.1016/j.asoc.2020.106275.

E. Lughofer, Evolving Fuzzy Systems: Methodologies, Advanced Concepts and Applications, Heidelberg, Germany: Springer, 2011, https://doi.org/10.1007/978-3-642-18087-3.

N. Kasabov, Evolving Connectionist Systems: The Knowledge Engineering Approach, 2nd ed. London, U.K.: Springer, 2007.

J. Kacprzyk and S. Zadrożny, “A hybrid systematic review approach on complexity issues in data-driven fuzzy inference systems development,” Informatica, vol. 29, no. 4, pp. 595–613, 2018.

G. Bucci, F. Ciancetta, E. Fiorucci, S. Mari, and A. Fioravanti, “Measurements for non-intrusive load monitoring through machine learning approaches,” ACTA IMEKO, vol. 10, no. 4, pp. 90–96, 2021, https://doi.org/10.21014/acta_imeko.v10i4.1184.

V. Golovko, Y. Savitsky, T. Laopoulos, A. Sachenko, and L. Grandinetti, “Technique of learning rate estimation for efficient training of MLP,” Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN), Como, Italy, 2000, vol. 1, pp. 323–328, https://doi.org/10.1109/IJCNN.2000.857856.

R. Prasad and P. K. Shukla, “Indeterminacy handling of adaptive neuro-fuzzy inference system using neutrosophic set theory: A case study for the classification of diabetes mellitus,” International Journal of Intelligent Systems and Applications, vol. 15, no. 3, pp. 1–15, 2023, https://doi.org/10.5815/ijisa.2023.03.01.

Downloads

Published

2026-01-01

How to Cite

Bodyanskiy, Y., Zolotukhin, O., Yerokhin, A., Kudryavtseva, M., & Yerokhin, M. (2026). Fast Stacking Neuro-Neo-Fuzzy System for Inverse Modeling in Online Mode. International Journal of Computing, 24(4), 661-667. Retrieved from https://www.computingonline.net/computing/article/view/4330

Issue

2025, Volume 24, Issue 4

Section

Articles

License

International Journal of Computing is an open access journal. 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 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.