Skip to main content

Basic Components of Neuronetworks with Parallel Vertical Group Data Real-Time Processing

  • Conference paper
  • First Online:
Advances in Intelligent Systems and Computing II (CSIT 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 689))

Included in the following conference series:

Abstract

Neuroalgorithms and neuronetwork structures were analyzed, basic components of neuronetworks were defined and the principles of their development were chosen. It was shown that using the method of parallel vertical group data processing for the implementation of the neuronetworks basic components provides speed increase, reduce of hardware costs and increasing of the equipment use efficiency. Parallel vertical group codes converter, which provides time alignment of data receipt processes and bit sections formation, was developed. The methods and the structures of the components with parallel vertical group data processing for definition of maximum and minimum numbers in the arrays, calculation of the sum of differences squares and scalar product, which due to parallel processing of bit sections groups, provide speed increase, were developed. It was shown that use of the developed basic components for neuronetworks synthesis will provide reduction of time and development cost.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mohamad, H.: Hassoun Fundamentals of Artificial Neural Networks, p. 511. MIT Press, Cambridge (1995)

    Google Scholar 

  2. Teich, T., Roessler, F., Kretz, D., Frank, S.: Design of a prototype neural network for smart homes and energy efficiency. In: Proceedings of 24th DAAAM International Symposium on Intelligent Manufacturing and Automation, pp. 603–608. Zwickau, Germany (2013)

    Google Scholar 

  3. Цюй Дyньюэ. Упpaвлeниe мoбильным poбoтoм нa ocнoвe нeчeткиx мoдeлeй / Цюй Дyньюэ // Coвpeмeнныe пpoблeмы нayки и oбpaзoвaния. – № 6. – C, pp. 115–121 (2012)

    Google Scholar 

  4. Matviichuk, K., Teslyuk, V., Teslyuk, T.: Vision system model for mobile robotic systems. In: Proceeding of the XIIh International Conference “Perspective Technologies and Methods in MEMS Design”, MEMSTECH 2016, 20–24 April 2016, pp. 104–106. Polyana, Lviv, Ukraine (2016)

    Google Scholar 

  5. Cagnoni, S., Coppini, G., Rucci, M., et al.: Neural network segmentation of magnetic resonance spin echo images of the brain. J. Biomed. Eng. 15(5), 355–362 (1993)

    Article  Google Scholar 

  6. Fujita, H., Katafuchi, T., Uehara, T., et al.: Application of artificial neural network to computer-aided diagnosis of coronary artery disease in myocardial SPECT bull’s-eye images. J. Nucl. Med. 33(2), 272–276 (1992)

    Google Scholar 

  7. Astion, M.L., Wener, M.H., Thomas, R.G., Hunder, G.G., Bloch, D.A.: Application of neural networks to the classification of giant cell arteritis. Arthritis Reum. 37(5), 760–770 (1994)

    Article  Google Scholar 

  8. Peleshko, D., Ivanov, Y., Sharov, B., Izonin, I., Borzov, Y.: Design and implementation of visitors queue density analysis and registration method for retail videosurveillance purposes. In: 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP), pp. 159–162. Lviv, Ukraine (2016)

    Google Scholar 

  9. Badlani, A., Bhano, S.: Smart home system design based on artificial neural networks. In: Proceedings of the World Congress on Engineering and Computer Science 2011, pp. 106–111. San Francisco, USA, 19–21 October 2011

    Google Scholar 

  10. Pukach, A.I., Teslyuk, V.M., Tkachenko, R.O., Ivantsiv, R.-A.D.: Implementation of neural networks for fuzzy and semistructured data. In: Proceedings of 11-th International Conference on the Experience of Designing and Application of CAD Systems in Microelectronics, CADSM 2011, pp. 350–352. Lviv, Polyana, Ukraine, 23–25 February 2011

    Google Scholar 

  11. Ocoвcкий C. Heйpoнныe ceти для oбpaбoтки инфopмaции / Пep. c пoльcкoгo. – M.: Финaнcы и cтaтиcтикa, 344 c (2009)

    Google Scholar 

  12. Пaтeнт №101922 Укpaїнa, G06F 7/38. Пpиcтpiй для oбчиcлeння cкaляpнoгo дoбyткy/ Цмoць I.Г., Cкopoxoдa O.B., Tecлюк B.M. Бюл. №9 (2013)

    Google Scholar 

  13. Пaтeнт №110187 Укpaїнa, G06F 7/38. Пpиcтpiй для визнaчeння мaкcимaльнoгo чиcлa з гpyпи чиceл/ Цмoць I.Г., Cкopoxoдa O.B., Meдикoвcький M.O., Aнтoнiв B.Я. Бюл. №22 (2015)

    Google Scholar 

  14. Цмoць I.Г. Moдифiкoвaний мeтoд тa HBIC - cтpyктypa пpиcтpoю гpyпoвoгo пiдcyмoвyвaння для нeйpoeлeмeнтa. / I.Г. Цмoць, O.B. Cкopoxoдa, Б.I. Бaлич // Bicник HУ « Львiвcькa пoлiтexнiкa » – Львiв. – № 732: « Кoмп’ютepнi нayки тa iнфopмaцiйнi тexнoлoгiї » . – C, pp. 51–57 (2012)

    Google Scholar 

  15. Tsmots, I., Skorokhoda, O., Rabyk, V.: Structure software model of a parallel-vertical multi-input adder for FPGA implementation. In: Proceedings of XIth International Scientific and Technical Conference CSIT 2016, pp. 158–160. Lviv, Ukraine, 6–10 September 2016

    Google Scholar 

  16. Цмoць I.Г., Cкopoxoдa O.B., Iгнaтєв I.B. Cинтeз кoмпoнeнтiв пapaлeльниx нeйpoмepeж вepтикaльнo-гpyпoвoгo типy. Bicник HУ “Львiвcькa пoлiтexнiкa” “Кoмп’ютepнi нayки тa iнфopмaцiйнi тexнoлoгiї” № 826 Львiв. C. pp. 69–79 (2015)

    Google Scholar 

  17. Bodyanskiy, Y., Dolotov, A., Vynokurova, O.: Evolving spiking wavelet-neuro-fuzzy self-learning system. Appl. Soft Comput. 14, 252–258 (2014)

    Article  Google Scholar 

  18. Bodyanskiy, Y., Vynokurova, O., Pliss, I., Peleshko, D., Rashkevych, Y.: Hybrid generalized additive wavelet-neuro-fuzzy-system and its adaptive learning. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds.) Dependability Engineering and Complex Systems: Proceedings of the Eleventh International Conference on Dependability and Complex Systems DepCoS-RELCOMEX. 27 June 2016–1 July 2016, pp. 51–61. Brunow, Poland (2016)

    Google Scholar 

  19. Haar, A.: Zur Theorie der orthogonalen Funktionensysteme. Math. Ann. 69(3), 331–371 (1910)

    Article  MathSciNet  Google Scholar 

  20. Daubechies, I.: Ten Lectures on Wavelets, SIAM, p. 194 (1992)

    Google Scholar 

  21. Galushkin, A.I.: Neurocomputers. Book 3 – M.: IPRZR, p. 528 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Ivan Tsmots , Vasyl Teslyuk , Taras Teslyuk or Ihor Ihnatyev .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Tsmots, I., Teslyuk, V., Teslyuk, T., Ihnatyev, I. (2018). Basic Components of Neuronetworks with Parallel Vertical Group Data Real-Time Processing. In: Shakhovska, N., Stepashko, V. (eds) Advances in Intelligent Systems and Computing II. CSIT 2017. Advances in Intelligent Systems and Computing, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-70581-1_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70581-1_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70580-4

  • Online ISBN: 978-3-319-70581-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics