GENETIC TUNING OF HIERARCHICAL MODELS BASED ON EXPERIMENTAL DATA.

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Bibliographic Details
Title:	GENETIC TUNING OF HIERARCHICAL MODELS BASED ON EXPERIMENTAL DATA.
Authors:	Mammadova, Kifayat¹ ka.mamedova@yandex.ru, Abasov, Ibrahim¹ abasov_i99@mail.ru, Ahmedov, Tural¹, Safaraliyev, Oqtay¹ oqtay.23@mail.ru
Source:	Reliability: Theory & Applications. 2025 Special Issue, p429-436. 8p.
Subjects:	Linguistic models, Membership functions (Fuzzy logic), Multilevel models, Knowledge base, Empirical research, Fuzzy logic, Genetic algorithms
Abstract:	The multidirectional search method is applied to the individual elements of the experimental data, forming and observing potential solution sets. The aim of this study is to genetically tune linguistic models using experimental data based on a fuzzy knowledge base. Experiential knowledge is derived from experience or observation. This process manifests itself in many different situations. It can be observed and predicted using qualitative characteristics. However, the process is not always understandable in terms of fundamental principles. For the purpose of forming primary information, the article establishes a fuzzy model that determines the linguistic values of variables and the formation of their membership functions. A knowledge matrix is drawn up according to the rules connecting the input and output of the identified object, and knowledge is determined based on logical considerations of the form 'IF-THENOTHERWISE'. Based on the matrix, the values of the input variables x_i are associated with one of the possible types of solution d_j. This problem is solved using fuzzy logic equations. These equations are based on a knowledge base or a system of logical judgements that is similar to it. It also allowed the membership functions of various solutions to be calculated at fixed input data values for the object. Typically, a solution with a high membership function value is considered the desired solution. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	The multidirectional search method is applied to the individual elements of the experimental data, forming and observing potential solution sets. The aim of this study is to genetically tune linguistic models using experimental data based on a fuzzy knowledge base. Experiential knowledge is derived from experience or observation. This process manifests itself in many different situations. It can be observed and predicted using qualitative characteristics. However, the process is not always understandable in terms of fundamental principles. For the purpose of forming primary information, the article establishes a fuzzy model that determines the linguistic values of variables and the formation of their membership functions. A knowledge matrix is drawn up according to the rules connecting the input and output of the identified object, and knowledge is determined based on logical considerations of the form 'IF-THENOTHERWISE'. Based on the matrix, the values of the input variables x_i are associated with one of the possible types of solution d_j. This problem is solved using fuzzy logic equations. These equations are based on a knowledge base or a system of logical judgements that is similar to it. It also allowed the membership functions of various solutions to be calculated at fixed input data values for the object. Typically, a solution with a high membership function value is considered the desired solution. [ABSTRACT FROM AUTHOR]
ISSN:	19322321