Development of regression technology for assessing the state of semi-Markov systems under conditions of a small sample of initial data

Authors

DOI:

https://doi.org/10.15587/2706-5448.2026.363099

Keywords:

semi-Markov systems, regression polynomial, small sample, artificial orthogonalization, representative subdesign

Abstract

The object of research is to assess the state of semi-Markov systems in conditions of a small sample of initial data.

This paper addresses the problem of assessing the state of a multi-element multifactorial stochastic object under conditions of a small sample of fuzzy initial data. Known methods for solving similar problems are ineffective in practical conditions of a small sample of initial data. A real possibility for identifying the relationship between explanatory and explained variables lies in the use of artificial orthogonalization of the results of a passive experiment. In this case, a full factorial experiment design is constructed, the most important property of which is orthogonality. This makes it possible to independently evaluate all the coefficients of the regression equation, which determine the degree of influence of factors and all their interactions on the value of the explained variable. This is achieved through the development of a technology for artificial orthogonalization of a passive experiment and a method for processing the resulting full factorial experiment design. The resulting heterogeneity of the full design is mitigated by finding a truncated representative orthogonal subdesign. The research resulted in a method that enables the calculation of all coefficients of the full regression equation with a small sample of initial data. This method provides a more accurate solution to the problem compared to known methods. The universality of the method lies in the fact that it is implemented in the same way for any set of objective functions. The ability of this method to overcome the computational complexity arising from the large dimensionality of relevant problems allows it to be applied in a wide range of practical areas. The proposed methodology is illustrated step by step by solving an example.

Author Biographies

Lev Raskin, National Technical University “Kharkiv Polytechnic Institute”

Doctor of Technical Sciences, Professor

Department of Software Engineering and Management Intelligent Technologies

Oksana Sira, National Technical University “Kharkiv Polytechnic Institute”

Doctor of Technical Sciences, Professor

Department of Computer Mathematics and Data Analysis

Larysa Sukhomlyn, Kremenchuk Mykhailo Ostrohradskyi National University

PhD, Associate Professor

Department of Management and Marketing

 

Vitalii Vlasenko, National Technical University “Kharkiv Polytechnic Institute”

PhD Student

Department of Software Engineering and Management Intelligent Technologies

Ihor Pryshchepa, National Technical University “Kharkiv Polytechnic Institute”

PhD Student

Department of Software Engineering and Management Intelligent Technologies

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Development of regression technology for assessing the state of semi-Markov systems under conditions of a small sample of initial data

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Published

2026-06-05

How to Cite

Raskin, L., Sira, O., Sukhomlyn, L., Vlasenko, V., & Pryshchepa, I. (2026). Development of regression technology for assessing the state of semi-Markov systems under conditions of a small sample of initial data. Technology Audit and Production Reserves, 3(2(89), 113–120. https://doi.org/10.15587/2706-5448.2026.363099

Issue

Vol. 3 No. 2(89) (2026): Information and control systems

Section

Systems and Control Processes

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Copyright (c) 2026 Lev Raskin, Oksana Sira, Larysa Sukhomlyn, Vitalii Vlasenko, Ihor Pryshchepa

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