PARALLEL COMPUTING SYSTEM RESOURCES PLANNING FOR NEURO-FUZZY MODELS SYNTHESIS AND BIG DATA PROCESSING

Authors

  • A. A. Oliinyk Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine, Ukraine
  • S.Yu. Skrupsky Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine, Ukraine
  • S. A. Subbotin Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine, Ukraine
  • O. Yu. Blagodariov Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine, Ukraine
  • Ye. A. Gofman Zaporizhzhia National Technical University, Zaporizhzhia, Ukraine, Ukraine

DOI:

https://doi.org/10.15588/1607-3274-2016-4-8

Keywords:

data sample, parallel computing, resource planning, neuro-fuzzy models, neural network.

Abstract

The article deals with the problem of planning resources of parallel computer systems for the synthesis of neuro-fuzzy networks. The
object of research is a process of synthesis of neuro-fuzzy models. The subject of research are the methods of resource planning of parallel
computer systems. The purpose of the work is to construct a model of parallel computing systems for resource planning, carrying out the
decision of practical applications based on parallel method of neuro-fuzzy networks synthesis. A model of parallel computer systems resource planning for the synthesis of neuro-fuzzy networks is proposed. Synthesized model takes into account the type of computer system, the number of processes in which the task is executed, the capacity of data network, the parameters of the mathematical software (number of possible solutions to be processed during the operation of the method, the proportion of solutions generated in each iteration of stochastic search through the use of crossover and mutation operator), as well as parameters of the solved applied problem (the number of observations and the number of features in a given data sample, which describes the results of observing the researching object or process). The software that implements a synthesized model of resource planning is developed. Experiments confirming the adequacy of the proposed model are executed. The experimental results allow us to recommend the usage of the developed model in practice.

References

Nauck D. Foundations of neuro-fuzzy systems / D. Nauck, F. Klawonn, R. Kruse. – Chichester : John Wiley & Sons, 1997. – 305 p. 2. Бодянський Є. В. Еволюційна каскадна система на основі нейро-фаззі вузлів / Є. В. Бодянський, О. К. Тищенко, О. О. Бойко // Радіоелектроніка, інформатика, управління. – 2016. – № 2. – С. 40–45. 3. Субботин С. А. Метод синтеза диагностических моделей на основе радиально-базисных нейронных сетей с поддержкой обобщающих свойств / С. А. Субботин // Радіоелектроніка, інформатика, управління. – 2016. – № 2. – С. 64–69. 4. Oliinyk A. O. Synthesis of Neuro-Fuzzy Networks on the Basis of Association Rules / A. O. Oliinyk, T. A. Zayko, S. A. Subbotin // Cybernetics and Systems Analysis. – 2014. – Vol. 50, Issue 3. – P. 348–357. DOI: 10.1007/s10559-014-9623-7. 5. Oliinyk A. Training Sample Reduction Based on Association Rules for Neuro-Fuzzy Networks Synthesis / A. Oliinyk, T. Zaiko, S. Subbotin // Optical Memory and Neural Networks (Information Optics). – 2014. – Vol. 23, № 2. – P. 89–95. DOI: 10.3103/S1060992X14020039. 6. Oliinyk A. The decision tree construction based on a stochastic search for the neuro-fuzzy network synthesis / A. Oliinyk, S. A. Subbotin // Optical Memory and Neural Networks (Information Optics). – 2015. – Vol. 24, № 1. – P. 18–27. DOI: 10.3103/S1060992X15010038. 7. Олійник А. О. Видобування продукційних правил на основі негативного відбору / А. О. Олійник // Радіоелектроніка, інфор- матика, управління. – 2016. – № 1. – С. 40–49. 8. Oliinyk A. O. Using Parallel Random Search to Train Fuzzy Neural Networks / A. O. Oliinyk, S. Yu. Skrupsky, S. A. Subbotin // Automatic Control and Computer Sciences. – 2014. – Vol. 48, Issue 6. – P. 313–323. DOI: 10.3103/S0146411614060078. 9. Oliinyk A. O. Experimental Investigation with Analyzing the Training Method Complexity of Neuro-Fuzzy Networks Based on Parallel Random Search / A. O. Oliinyk, S. Yu. Skrupsky, S. A. Subbotin // Automatic Control and Computer Sciences. – 2015. – Vol. 49, Issue 1. – P. 11–20. DOI: 10.3103/ S0146411615010071. 10. Subbotin S. Individual prediction of the hypertensive patient condition based on computational intelligence / S. Subbotin, A. Oliinyk, S. Skrupsky // Information and Digital Technologies : International Conference IDT’2015, Zilina, 7–9 July 2015 : proceedings of the conference. – Zilina : Institute of Electrical and Electronics Engineers, 2015. – P. 336–344. DOI: 10.1109/DT.2015.7222996. 11. Sulistio A. Simulation of Parallel and Distributed Systems: A Taxonomy – and Survey of Tools / A. Sulistio, C.S. Yeo. R. Buyya // International Journal of Software Practice and Experience. Wiley Press. – 2002. – P. 1–19. 12. Методы и модели планирования ресурсов в GRID-системах : монография / [В. С. Пономаренко, С. В. Листровой, С. В. Минухин, С.В. Знахур]. – X. : ИД «ИНЖЭК», 2008. – 408 с. 13.Introduction to GPUs. – Режим доступа: URL: https://www.cs .utexas .edu/~pingali/CS378/2015sp/lectures / IntroGPUs.pdf. – Загл. з екрану. 14. Gebali F. Algorithms and Parallel Computing / F. Gebali. – New Jersey : John Wiley & Sons, 2011. – 364 р. DOI: 10.1002/ 9780470932025. 15. Fokkink W. Distributed Algorithms: An Intuitive Approach / Wan Fokkink. – Cambridge : MIT Press, 2013. – 248 p. 16. Herlihy M. The Art of Multiprocessor Programming Revised Reprint / M. Herlihy, N. Shavit. – Boston : Morgan Kaufmann, 2012. – 536 p.

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How to Cite

Oliinyk, A. A., Skrupsky, S., Subbotin, S. A., Blagodariov, O. Y., & Gofman, Y. A. (2017). PARALLEL COMPUTING SYSTEM RESOURCES PLANNING FOR NEURO-FUZZY MODELS SYNTHESIS AND BIG DATA PROCESSING. Radio Electronics, Computer Science, Control, (4). https://doi.org/10.15588/1607-3274-2016-4-8

Issue

No. 4 (2016): Radio Electronics, Computer Science, Control

Section

Neuroinformatics and intelligent systems

License

Copyright (c) 2017 A. A. Oliinyk, S.Yu. Skrupsky, S. A. Subbotin, O. Yu. Blagodariov, Ye. A. Gofman

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