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

A. A. Oliinyk, S.Yu. Skrupsky, S. A. Subbotin, O. Yu. Blagodariov, Ye. A. Gofman

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.

Keywords


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

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



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