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

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.

Innovative System of Computer Modelling of Multiphysics Processes for Controlled Electrocurrent Treatment of Melts

“…By codifying the characteristic features of MT as system (classification) attributes of TC, which determine the disposition of modeling, the implementation of functional connections between modules in SCM is ensured. The introduction of such codification is similar to the synthesis technique for a computational library of modeled objects masks, used in [17] for analogous purposes. So, methodological sense of introduction of TC is that its attributes identify the CMs that are adequate for a certain MT, and incorporate them into the ITIS algorithmic environment using software (protocols)of data exchange with BCM, where NLM are formed.…”

Machine Learning Techniques for Increasing Efficiency of the Robot’s Sensor and Control Information Processing

“…The above-discussed analysis of last publications on the implementation of machine learning in robotics shows that researchers continue to improve machine learning techniques [ 47 , 80 , 81 ] and develop new machine learning solutions [ 40 , 82 , 83 , 84 , 85 ] for intelligent robots using well-known and new design methods, approaches, and methodologies. It deals, first, with the new and specific missions of the ground, underwater, and aerial mobile robots, as well as with the high level of information uncertainty concerning the nature of the robot environment, changing character of manipulated object parameters, and unknown behavior of the dynamic obstacles.…”