Real-Time Fuzzy Data Processing Based on a Computational Library of Analytic Models

Yuriy Kondratenko,Nina Kondratenko

doi:10.3390/data3040059

Yuriy Kondratenko, Nina Kondratenko

Open Access

https://doi.org/10.3390/data3040059

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Abstract

This work focuses on fuzzy data processing in control and decision-making systems based on the transformation of real-timeseries and high-frequency data to fuzzy sets with further implementation of diverse fuzzy arithmetic operations. Special attention was paid to the synthesis of the computational library of horizontal and vertical analytic models for fuzzy sets as the results of fuzzy arithmetic operations. The usage of the developed computational library allows increasing the operating speed and accuracy of fuzzy data processing in real time. A computational library was formed for computing of such fuzzy arithmetic operations as fuzzy-maximum. Fuzzy sets as components of fuzzy data processing were chosen as triangular fuzzy numbers. The analytic models were developed based on the analysis of the intersection points between left and right branches of considered triangular fuzzy numbers with different relations between their parameters. Our study introduces the mask for the evaluation of the relations between corresponding parameters of fuzzy numbers that allows to determine the appropriate model from the computational library in automatic mode. The simulation results confirm the efficiency of the proposed computational library for different applications.

Highlights

Increasing the efficiency of the real-time control systems and decision-making processes under uncertain conditions deals with creating new techniques for Big Data processing, management, and analysis taking into account the dynamic nature of real objects’ signals and information [1,2,3].To this day, there are some successful mathematical methods, algorithms, and approaches developed based on the theory of computational intelligence, machine learning, soft computing, and recent advancements in cognitive computing [4,5,6,7]
Special attention should be paid to the application of the theory of fuzzy sets, fuzzy logic, and fuzzy optimization as powerful tools for Big Data analysis and processing in terms of solving real-world problems in uncertain or fuzzy conditions [8,9,10]
This research aims to propose the advancements in the construction of the universal horizontal and vertical analytic models of the resulting MFs as main components of the generalized computational library that provide (a) automatic choice of the desired analytic models from the computational library based on the relationships between parameters of the initial fuzzy sets for fuzzy data processing and (b) improvement in the operating velocity and accuracy of the fuzzy arithmetic operations with special attention to FNs-maximum as one of the most difficult and complex arithmetic operations

Summary

Introduction

Increasing the efficiency of the real-time control systems and decision-making processes under uncertain conditions deals with creating new techniques for Big Data processing, management, and analysis taking into account the dynamic nature of real objects’ signals and information [1,2,3]. This research aims to propose the advancements in the construction of the universal horizontal and vertical analytic models of the resulting MFs as main components of the generalized computational library that provide (a) automatic choice of the desired analytic models from the computational library based on the relationships between parameters of the initial fuzzy sets for fuzzy data processing and (b) improvement in the operating velocity and accuracy of the fuzzy arithmetic operations with special attention to FNs-maximum (maximum of fuzzy numbers) as one of the most difficult and complex (in computing aspects) arithmetic operations.

Problem Statement

FNs-Maximum andD D

Conclusions