EXPERIMENTAL INVESTIGATION OF METHOD FOR THE SYNTHESIS OF NEURO-FUZZY MODELS IN A PARALLEL COMPUTER SYSTEM

S. Yu. Skrupsky

Abstract


The article deals with the problem of the development of the non-linear model describing dependences between the characteristics of a
system, in which synthesis of neuro-fuzzy networks is realized, the parameters of the investigated method and the time spent on execution of
the models synthesis. The object of research is a synthesis of neuro-fuzzy models for individual prediction of the hypertensive patient state.
The subject of research is a parallel computer system that performs the method of neuro-fuzzy networks synthesis. The purpose of the work
is to improve the efficiency of parallel computer systems solving the problems of medical direction. A non-linear model to predict the time
used by a parallel system to perform the method of neuro-fuzzy network synthesis and thus to execute a rational choice of the computer system
resources has been proposed. The software that implements the proposed model has been developed. Experiments confirming the adequacy of the proposed model have been executed. The experimental results allow us to recommend the application of the developed model in practice.

Keywords


synthesis of model, parallel system, resource planning, neural network, mean-squared error.

References


Лашкул З. В. Особливості епідеміології артеріальної гіпертензії та її ускладнень на регіональному рівні з 1999 по 2013 роки / З. В. Лашкул // Сучаснi медичнi технології. – 2014, № 2. – С. 134–141. 2. Sergey Subbotin Individual prediction of the hypertensive patient condition based on computational intelligence / Sergey Subbotin, Andrii Oliinyk, Stepan Skrupsky – Information and Digital Technologies 2015 ISBN 978-1-4673-7185-8, 7–9 july 2015, Zilina, Slovakia. – P. 336–344 3. Oliinyk A. O. Experimental Investigation with Analyzing the Training Method Complexity of Neuro-Fuzzy Networks Based on Parallel Random Search / Andrii Oliinyk, Stepan Skrupsky, Sergey Subbotin // Automatic Control and Computer Sciences ISSN 0146-4116. – 2015. Vol. 49, No. 1. – P. 11–20. DOI: 10.3103/S0146411615010071 4. Субботін С. О. Подання й обробка знань у системах штучного інтелекту та підтримки прийняття рішень : навч. посібник / С. О. Субботін. – Запоріжжя : ЗНТУ, 2008. – 341 с. 5. 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 6. Характеристики ГРІД-вузла НАНУ. – Режим доступа: URL: http://www.ipme.kiev.ua/ukr/grid_vuzol/charakter-g.html. – Загл. з екрану. 7. Introduction to GPUs. – Режим доступа: URL: https://www.cs.u texa s.edu /~pinga l i /CS37 8 /20 1 5 sp/ lect u res/IntroGPUs.pdf. – Загл. з екрану. 8. 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. 9. 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. 10. Петренко А. І. Комп’ютерне моделювання ГРІД-систем / А. I. Петренко // Электроника и связь 5' Тематический выпуск «Электроника и нанотехнологии». – 2010. – С. 40–48.


GOST Style Citations






DOI: https://doi.org/10.15588/1607-3274-2016-2-7



Copyright (c) 2016 S. Yu. Skrupsky

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

Address of the journal editorial office:
Editorial office of the journal «Radio Electronics, Computer Science, Control»,
Zaporizhzhya National Technical University, 
Zhukovskiy street, 64, Zaporizhzhya, 69063, Ukraine. 
Telephone: +38-061-769-82-96 – the Editing and Publishing Department.
E-mail: rvv@zntu.edu.ua

The reference to the journal is obligatory in the cases of complete or partial use of its materials.