Дослідження інтелектуальних моделей управління на основі процедур класифікації невизначених даних зі встановленими вимогами достовірності результатів
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Український державний університет науки і технологій, ННІ ≪Інститут промислових та бізнес технологій≫, ІВК ≪Системні технології≫, Дніпро
Abstract
UKR: Стаття присвячена дослідженням властивостей і розвитку інтелектуальних моделей управління складними системами за умов невизначеності даних на основі процедур класифікації на основі методів редукції та статистики каппа Коена. Застосування цих методів забезпечує достовірне вирішення завдань з урахуванням оцінки граничної розмірності моделей класифікації. В роботі були досліджені можливості удосконалення нейронних мереж Хеммінга для класифікації даних у форматах нечітких величин і certainty factor CF(A). Також були визначені особливості удосконаленої математичної моделі завдань нечіткої класифікації на основі набору шаблонів ознак. Представлено структуру програмного комплексу інформаційної технології управління призначенням/відбором виконавців на основі класифікації наборів шаблонів із певних нечітких ознак, який використовує процедури редукції і каппа статистики.
ENG: For a wide range of complex systems, tasks such as selection of control options for various technological processes, selection of performers for assigned tasks, and determination of authorship are resolved through classification and diagnosis of incomplete data regarding states and conditions of operation. The relevant problems include forming adequate mathematical models of classification procedures and establishing their correctness, completeness, and reliability of results. This article focuses on investigating the properties and development of intellectual management models for complex systems under conditions of data uncertainty based on classification procedures using reduction methods and Cohen's kappa statistics. It is noted that the application of these methods ensures reliable resolution of classification tasks considering the assessment of the maximum model dimensionality. Additionally, the possibilities of improving Hamming neural networks intended for data classification tasks in formats of fuzzy values and certainty factors CF(A) were explored. The features of the proposed enhanced mathematical model for fuzzy classification tasks based on a set of feature templates defining the classes of objects under analysis were identified. The article also discusses the peculiarities of the mathematical model of classification designed for the task of determining the authorship of Ukrainian-language works (UAW). The characteristics of the UAW task and its implementation based on a fuzzy classification model include the absence of requirements regarding the number of stages in the authorship determination procedure, the unnecessary formation of a unified classification model for UAW tasks for any possible input works, and the absence of the need to transform template models when introducing new data or works into the model. The listed features of classification procedures are accounted for in the reduction and Cohen's kappa procedures outlined in the article. To implement and study classification tasks of complex system parameters under conditions of uncertain data, appropriate software was developed. The article presents the structure of the software complex for information technology management of performer assignment/selection, as well as the task of determining authorship of Ukrainian-language works based on classification of sets of templates with certain fuzzy features. The software complex utilizes reduction and kappa statistics procedures.
ENG: For a wide range of complex systems, tasks such as selection of control options for various technological processes, selection of performers for assigned tasks, and determination of authorship are resolved through classification and diagnosis of incomplete data regarding states and conditions of operation. The relevant problems include forming adequate mathematical models of classification procedures and establishing their correctness, completeness, and reliability of results. This article focuses on investigating the properties and development of intellectual management models for complex systems under conditions of data uncertainty based on classification procedures using reduction methods and Cohen's kappa statistics. It is noted that the application of these methods ensures reliable resolution of classification tasks considering the assessment of the maximum model dimensionality. Additionally, the possibilities of improving Hamming neural networks intended for data classification tasks in formats of fuzzy values and certainty factors CF(A) were explored. The features of the proposed enhanced mathematical model for fuzzy classification tasks based on a set of feature templates defining the classes of objects under analysis were identified. The article also discusses the peculiarities of the mathematical model of classification designed for the task of determining the authorship of Ukrainian-language works (UAW). The characteristics of the UAW task and its implementation based on a fuzzy classification model include the absence of requirements regarding the number of stages in the authorship determination procedure, the unnecessary formation of a unified classification model for UAW tasks for any possible input works, and the absence of the need to transform template models when introducing new data or works into the model. The listed features of classification procedures are accounted for in the reduction and Cohen's kappa procedures outlined in the article. To implement and study classification tasks of complex system parameters under conditions of uncertain data, appropriate software was developed. The article presents the structure of the software complex for information technology management of performer assignment/selection, as well as the task of determining authorship of Ukrainian-language works based on classification of sets of templates with certain fuzzy features. The software complex utilizes reduction and kappa statistics procedures.
Description
В. Скалозуб: ORCID 0000-0002-1941-4751; В. Горячкін: ORCID 0000-0002-8952-952X; І. Клименко: ORCID 0000-0001-5149-3974
Keywords
класифікація наборів шаблонів, невизначені дані, редукція розмірності, статистика каппа Коена, нечіткі величини, модифікована мережа Хеммінга, інформаційна технологія, програмне забезпечення, завдання визначення авторства та призначення/відбір виконавця, template set classification, uncertain data, dimensionality reduction, Cohen's Kappa statistics, fuzzy values, certainty factor CF(A), modified Hamming network, information technology, software, authorship determination, performer assignment/selection, КІТ
Citation
Скалозуб В. В., Горячкін В. М., Клименко І. В., Терлецький І. А. Дослідження інтелектуальних моделей управління на основі процедур класифікації невизначених даних зі встановленими вимогами достовірності результатів. Системні технології. Дніпро, 2024. Т. 2, № 151. С. 155–171. DOI: 10.34185/1562-9945-2-151-2024-14.