Parallel data reduction method for complex technical objects and processes

This paper presents a method that implements parallel reduction of data based on the stochastic calculations. Significant attention is paid to the relevance of the developed method and the description of its features. For assessing the effectiveness of the method experimental comparison was carried out: the results are also presented in this paper.

[1]  A. A. Oliinyk,et al.  A stochastic approach for association rule extraction , 2016, Pattern Recognition and Image Analysis.

[2]  Peter Arras,et al.  The 7 Th Ieee International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications E-learning Concept for the Properties of Materials Remote Study , 2022 .

[3]  Yevgeniy V. Bodyanskiy,et al.  Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification , 2013, Inf. Sci..

[4]  Sergey Subbotin,et al.  The Dimensionality Reduction Methods Based on Computational Intelligence in Problems of Object Classification and Diagnosis , 2016, ICONS 2016.

[5]  Galyna Tabunshchyk,et al.  Flexible technologies for smart campus , 2016, 2016 13th International Conference on Remote Engineering and Virtual Instrumentation (REV).

[6]  V. Lovkin,et al.  Improved method of group decision making in expert systems based on competitive agents selection , 2017, 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON).

[7]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[8]  B. B. Samotokin,et al.  DEVELOPMENT OF STRATIFIED APPROACH TO SOFTWARE DEFINED NETWORKS SIMULATION , 2017 .

[9]  Aapo Hyvärinen,et al.  Independent Component Analysis: Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity , 2001 .

[10]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[11]  Andrii Oliinyk,et al.  Development of the method for decomposition of superpositions of unknown pulsed signals using the second­order adaptive spectral analysis , 2018 .

[12]  Michel Verleysen,et al.  Nonlinear Dimensionality Reduction , 2021, Computer Vision.

[13]  Александр Викторович Близняков,et al.  Increase effectiveness of reversible braking mode realization of the wound-rotor induction motor , 2015 .

[14]  Sergey Subbotin,et al.  The Sample and Instance Selection for Data Dimensionality Reduction , 2016, ICONS 2016.

[15]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[16]  Andrii Oliinyk,et al.  Agent technologies for feature selection , 2012 .

[17]  Qiang Shen,et al.  Computational Intelligence and Feature Selection - Rough and Fuzzy Approaches , 2008, IEEE Press series on computational intelligence.

[18]  M. Kotsur,et al.  A new approach of the induction motor parameters determination in short-circuit mode by 3D electromagnetic field simulation , 2017, 2017 IEEE International Young Scientists Forum on Applied Physics and Engineering (YSF).

[19]  Yung C. Shin,et al.  Intelligent Systems: Modeling, Optimization, and Control , 2008 .

[20]  Andrii O. Oliinyk,et al.  Factor analysis of transaction data bases , 2014, Automatic Control and Computer Sciences.