Information estimations and analysis of structures

TL;DR

This paper presents an information analysis method for structures using Shannon entropy, providing a steady, vector-based estimation applicable across various fields for ranking, evaluation, and optimization of structures.

Contribution

It introduces a novel information estimation approach based on Shannon entropy that is consistent for isomorphic and non-isomorphic graphs, with broad interdisciplinary applications.

Findings

Information estimation is univalent for different graph types.

The method is asymptotically steady and vector-characterized.

Applicable in diverse fields like electronics, biology, and data analysis.

Abstract

In this paper have written the results of the information analysis of structures. The obtained information estimation (IE) are based on an entropy measure of C. Shannon. Obtained IE is univalent both for the non-isomorphic and for the isomorphic graphs, algorithmically, it is asymptotically steady and has vector character. IE can be used for the solution of the problems ranking of structures by the preference, the evaluation of the structurization of subject area, the solution of the problems of structural optimization. Information estimations and method of the information analysis of structures it can be used in many fields of knowledge (Electrical Systems and Circuit, Image recognition, Computer technology, Databases and Bases of knowledge, Organic chemistry, Biology and others) and it can be base for the structure calculus.