GENETIC TUNING OF HIERARCHICAL MODELS BASED ON EXPERIMENTAL DATA.
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| Title: | GENETIC TUNING OF HIERARCHICAL MODELS BASED ON EXPERIMENTAL DATA. |
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| 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] |
| Copyright of Reliability: Theory & Applications is the property of International Group on Reliability and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: GENETIC TUNING OF HIERARCHICAL MODELS BASED ON EXPERIMENTAL DATA. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mammadova%2C+Kifayat%22">Mammadova, Kifayat</searchLink><relatesTo>1</relatesTo><i> ka.mamedova@yandex.ru</i><br /><searchLink fieldCode="AR" term="%22Abasov%2C+Ibrahim%22">Abasov, Ibrahim</searchLink><relatesTo>1</relatesTo><i> abasov_i99@mail.ru</i><br /><searchLink fieldCode="AR" term="%22Ahmedov%2C+Tural%22">Ahmedov, Tural</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Safaraliyev%2C+Oqtay%22">Safaraliyev, Oqtay</searchLink><relatesTo>1</relatesTo><i> oqtay.23@mail.ru</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Reliability%3A+Theory+%26+Applications%22">Reliability: Theory & Applications</searchLink>. 2025 Special Issue, p429-436. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Linguistic+models%22">Linguistic models</searchLink><br /><searchLink fieldCode="DE" term="%22Membership+functions+%28Fuzzy+logic%29%22">Membership functions (Fuzzy logic)</searchLink><br /><searchLink fieldCode="DE" term="%22Multilevel+models%22">Multilevel models</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+base%22">Knowledge base</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br /><searchLink fieldCode="DE" term="%22Genetic+algorithms%22">Genetic algorithms</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Reliability: Theory & Applications is the property of International Group on Reliability and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 429 Subjects: – SubjectFull: Linguistic models Type: general – SubjectFull: Membership functions (Fuzzy logic) Type: general – SubjectFull: Multilevel models Type: general – SubjectFull: Knowledge base Type: general – SubjectFull: Empirical research Type: general – SubjectFull: Fuzzy logic Type: general – SubjectFull: Genetic algorithms Type: general Titles: – TitleFull: GENETIC TUNING OF HIERARCHICAL MODELS BASED ON EXPERIMENTAL DATA. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mammadova, Kifayat – PersonEntity: Name: NameFull: Abasov, Ibrahim – PersonEntity: Name: NameFull: Ahmedov, Tural – PersonEntity: Name: NameFull: Safaraliyev, Oqtay IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 11 Text: 2025 Special Issue Type: published Y: 2025 Identifiers: – Type: issn-print Value: 19322321 Titles: – TitleFull: Reliability: Theory & Applications Type: main |
| ResultId | 1 |
