Analysis of G-Transformation Modes for Building Neuro-like Parallel-Hierarchical Network Identification of Rail Surface Defects

被引：0

作者：

Lukosevicius, Vaidas ^{[1
]}

Tverdomed, Volodymyr ^{[2
]}

Tymchenko, Leonid ^{[2
]}

Kokriatska, Natalia ^{[2
]}

Didenko, Yurii ^{[2
]}

Demchenko, Mariia ^{[2
]}

Oliynyk, Olena ^{[2
]}

机构：

[1] Kaunas Univ Technol, Fac Mech Engn & Design, Dept Transport Engn, Studentu Str 56, LT-44249 Kaunas, Lithuania

[2] State Univ Infrastruct & Technol, Kyiv Inst Railway Transport, Kyrylivska Str 9, UA-04071 Kyiv, Ukraine

来源：

MATHEMATICS | 2025年 / 13卷 / 06期

关键词：

transformations; parallel-hierarchical networks; parallelism; signal processing; neural networks; rail surface defects; FAULT-TOLERANCE;

D O I：

10.3390/math13060966

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This work presents the construction of a transformation for the identification of surface defects on rails, starting with the selection of elements from the matrix and the creation of different matrices. It further elaborates on the recursive formulation of the transformation and demonstrates that, regardless of the elements' uniqueness, the sum of the transformed matrix remains equal to the sum of the original matrix. This study also addresses the handling of matrices with repeated elements and proves that the G-transformation preserves information, ensuring the integrity of data without any loss or redundancy.

引用

页数：15