Multidimensional Wavelet Neuron for Pattern Recognition Tasks in the Internet of Things Applications

  • Olena Vynokurova
  • Dmytro Peleshko
  • Semen Oskerko
  • Vitalii Lutsan
  • Marta Peleshko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)


Data mining and processing of Big Data is key problem in developing intelligent Internet of Things (IoT) applications. In this article, the multidimensional wavelet neuron for pattern recognition tasks is proposed. Also, the learning algorithm based on the quadratic error criterion is synthesized. This approach combines the benefits of the neural networks, the neuro-fuzzy system, and the wavelet functions approximation. The proposed multidimensional wavelet neuron can be used to solve a very large class of information processing problems for the Internet of Things applications when signals are fed in online mode from many sensors. The proposed approach is uncomplicated for computational realization and can be implemented in hardware for IoT systems. The proposed learning algorithm is characterized by a high rate of convergence and high approximation properties.


Machine learning Internet of Things Multidimensional wavelet neuron Pattern recognition Classification Online learning 


  1. 1.
    Mahdavinejad, M.S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., Sheth, A.P.: Machine learning for Internet of Things data analysis: a survey. Dig. Commun. Netw. (in Press, 2017)Google Scholar
  2. 2.
    Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefGoogle Scholar
  3. 3.
    Cecchinel, C., Jimenez, M., Mosser, S., Riveill, M.: An architecture to support the collection of big data in the Internet of Things. In: Proceedings of 2014 IEEE World Congress on Services, pp. 442–449 (2014)Google Scholar
  4. 4.
    Joakar, A.: A methodology for solving problems with data science for Internet of Things. Data Science for Internet of Things. Accessed 27 Nov 2017
  5. 5.
    Ni, P., Zhang, C., Ji, Y.: A hybrid method for short-term sensor data forecasting in Internet of Things. In: Proceedings of 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 369–373 (2014)Google Scholar
  6. 6.
    Alam, F., Mehmood, R., Katib, I., Albeshri, A.: Analysis of eight data mining algorithms for smarter Internet of Things (IoT). Procedia Comput. Sci. 98, 437–442 (2016)CrossRefGoogle Scholar
  7. 7.
    Bodyanskiy, Y., Vynokurova, O., Pliss, I., Peleshko, D.: Hybrid adaptive systems of computational intelligence and their on-line learning for green it in energy management tasks In: Kharchenko, V., Kondratenko, Y., Kacprzyk, J. (eds.) Green IT Engineering: Concepts, Models, Complex Systems Architectures. Studies in Systems, Decision and Control, vol. 74, pp. 229–244. Springer, Cham (2017)Google Scholar
  8. 8.
    Peleshko, D., Peleshko, M., Kustra, N., Izonin, I.: Analysis of invariant moments in tasks image processing. In: Proceedings of 2011 11th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), Polyana-Svalyava, pp. 263–264 (2011)Google Scholar
  9. 9.
    Ivanov, Y., Peleshko, D., Makoveychuk, O., Izonin I., Malets I., Lotoshunska, N, Batyuk, D. Adaptive moving object segmentation algorithms in cluttered environments. In.: Proceedings of 2015 15th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics (CADSM), Lviv, pp. 97–99 (2015)Google Scholar
  10. 10.
    Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer, Berlin (2008)CrossRefGoogle Scholar
  11. 11.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Berlin (2006)zbMATHGoogle Scholar
  12. 12.
    Murphy K.P.: Machine Learning: a Probabilistic Perspective. MIT Press (2012)Google Scholar
  13. 13.
    Bodyanskiy, Y.V., Vynokurova, O.A., Dolotov, A.I.: Self-learning cascade spiking neural network for fuzzy clustering based on group method of data handling. J. Autom. Inf. Sci. 45, 23–33 (2013)CrossRefGoogle Scholar
  14. 14.
    Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Tkachov, V.M.: Fuzzy clustering data arrays with omitted observations. Int. J. Intell. Syst. Appl. (IJISA) 9(6), 24–32 (2017). Scholar
  15. 15.
    Bodyanskiy, Y., Vynokurova, O., Setlak, G., Peleshko, D., Mulesa, P.: Adaptive multivariate hybrid neuro-fuzzy system and its on-board fast learning. Neurocomputing 230, 409–416 (2017)CrossRefGoogle Scholar
  16. 16.
    Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Samitova, V.O.: Possibilistic fuzzy clustering for categorical data arrays based on frequency prototypes and dissimilarity measures. Int. J. Intell. Syst. Appl. (IJISA) 9(5), 55–61 (2017). Scholar
  17. 17.
    Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Samitova, V.O.: Fuzzy clustering data given on the ordinal scale based on membership and likelihood functions sharing. Int. J. Intell. Syst. Appl. (IJISA) 9(2), 1–9 (2017). Scholar
  18. 18.
    Babichev, S., Taif, M.A., Lytvynenko, V.: Inductive model of data clustering based on the agglomerative hierarchical algorithm. In: Proceedings of 2016 IEEE 1st International Conference on Data Stream Mining and Processing, DSMP 2016, pp. 19–22 (2016)Google Scholar
  19. 19.
    Babichev, S., Taif, M.A., Lytvynenko, V., Osypenko, V.: Criterial analysis of gene expression sequences to create the objective clustering inductive technology. In: Proceedings of 2017 IEEE 37th International Conference on Electronics and Nanotechnology, ELNANO 2017, pp. 244–248 (2017)Google Scholar
  20. 20.
    Bodyanskiy, Y., Vynokurova, O., Pliss, I., Setlak, G., Mulesa, P.: Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks. In.: Proceedings of 2016 IEEE 1st International Conference on Data Stream Mining and Processing, DSMP 2016, pp. 257–262 (2016)Google Scholar
  21. 21.
    Hu, Z., Bodyanskiy, Y.V., Tyshchenko, O.K., Samitova, V.O.: Fuzzy clustering data given in the ordinal scale. Int. J. Intell. Syst. Appl. (IJISA) 9(1), 67–74 (2017). Scholar
  22. 22.
    Bodyanskiy, Y., Setlak, G., Peleshko, D., Vynokurova, O.: Hybrid generalized additive neuro-fuzzy system and its adaptive learning algorithms. In: Proceedings of 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS, pp. 328–333 (2015)Google Scholar
  23. 23.
    Perova, I., Pliss, I.: Deep hybrid system of computational intelligence with architecture adaptation for medical fuzzy diagnostics. Int. J. Intell. Syst. Appl. (IJISA) 9(7), 12–21 (2017). Scholar
  24. 24.
    Bin, S., Yuan, L., Xiaoyi, W.: Research on data mining models for the internet of things. In: Proceedings of 2010 IEEE International Conference on Image Analysis and Signal Processing, pp. 127–132 (2010)Google Scholar
  25. 25.
    Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A.V., Rong, X.: Data mining for the internet of things: literature review and challenges. Int. J. Distrib. Sens. Netw. 11(8), 1–14 (2015)Google Scholar
  26. 26.
    Tsai, C.-W., Lai, C.-F., Chiang, M.-C., Yang, L.T.: Data mining for internet of things: a survey. IEEE Commun. Surv. Tutor. 16(1), 77–97 (2014)CrossRefGoogle Scholar
  27. 27.
    Zanella, A., Bui, N., Castellani, A., Vangelista, L., Zorzi, M.: Internet of 905 Things for smart cities. IEEE Internet of Things J. 1(1), 22–32 (2014)CrossRefGoogle Scholar
  28. 28.
    Bodyanskiy, Y., Lamonova, N., Pliss, I., Vynokurova, O.: An adaptive learning algorithm for a wavelet neural network. Exp. Syst. 22(5), 235–240 (2005)CrossRefGoogle Scholar
  29. 29.
    Yamakawa, T., Uchino, E., Miki, T., Kusanagi, H.: A neo-fuzzy neuron and its application to system identification and prediction of the system behavior. In: Proceedings of 2nd International Conference on Fuzzy Logic and Neural Networks, pp. 477–483, Iizuka, Japan (1992)Google Scholar
  30. 30.
    Miki, I., Yamakawa, I.: Analog implementation of neo-fuzzy neuron and its on-board learning. In: Computational Intelligence and Applications. pp. 144–149. WSES Press, Piraeus (1999)Google Scholar
  31. 31.
    Mitaim, S., Kosko, B.: What is the best shape for a fuzzy set in function approximation? In: Proceedings of 5th IEEE International Conference on Fuzzy Systems, Fuzz 1996, vol. 2, pp. 1237–1213 (1996)Google Scholar
  32. 32.
    Shepherd, A.J.: Second-Order Methods for Neural Networks. Springer, London (1997)CrossRefGoogle Scholar
  33. 33.
    Bodyanskiy, Y., Kolodyazhniy, V., Stephan, A.: An adaptive learning algorithm for a neuro-fuzzy network. In: Reusch, B. (eds.) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. LNCS, vol. 2206, pp. 68–75. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  34. 34.
    Goodwin, G.C., Ramadge, P.J., Caines, P.E.: A globally convergent adaptive predictor. Automatica 17(1), 135–140 (1981)MathSciNetCrossRefGoogle Scholar
  35. 35.
    MNIST Homepage. Accessed 19 Nov 2017

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Olena Vynokurova
    • 1
  • Dmytro Peleshko
    • 1
  • Semen Oskerko
    • 1
  • Vitalii Lutsan
    • 1
  • Marta Peleshko
    • 2
  1. 1.IT Step UniversityLvivUkraine
  2. 2.Lviv State University of Life SafetyLvivUkraine

Personalised recommendations