Work

Methods for Synthesizing Optimal K-Value Correcting Functions

Year: 2026

Type: article

Abstract: This paper explores the problem of developing correcting functions that replace multiple unreliable (heuristic) recognition algorithms with a single, consistent decision function. The research concept stems from the fact that, in pattern recognition and collective expert decision-making problems, individual algorithms (heuristics) provide inconsistent or incomplete answers, requiring a mathematically sound method for their harmonization (correction) using test data. The methodological content of this paper consists of introducing classes of k-valued logic correcting functions that satisfy the constraints of preserving given sets (Constraint I) and monotonicity (Constraint II), and constructing algorithms for synthesizing a corrector that is optimal with respect to the linear performance functional for each combination of these constraints. The paper also provides a rationale for the optimality of the constructed algorithms (an optimality theorem) and an estimate of their time complexity.

Source: Mathematics

DOI: 10.3390/math14173070

Authors: Anvar Kabulov, Islambek Saymanov, Alimdzhan Babadzhanov, Nodirbek Urinov, Mansur Berdimurodov, Azimjon Arabboev, Madiyar Seidullayev

Institutions: National University of Uzbekistan, New Uzbekistan University, Andijan State University, International Islamic Academy of Uzbekistan, Kimyo International University in Tashkent, Tashkent University of Information Technology

Language: English

Topic: Advanced Algebra and Logic

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

OpenAlex ID: W7204259878

FWCI:

Cites: 29

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