Lesnoy Vestnik / Forestry BulletinLesnoy Vestnik / Forestry BulletinЛесной вестник / Forestry Bulletin2542-1468Eco-Vector70698110.18698/2542-1468-2024-3-133-140Math modelingМатематическое моделированиеResearch ArticleProcessing and prediction of educational process data based on fuzzy regression analysisОбработка и прогноз данных образовательного процесса на основе нечеткого регрессионного анализаPoleshchukOl’ga M.ПолещукОльга Митрофановна
Russian Federation

Dr. Sci. (Tech.), Professor, Head of Higher Mathematics and Physics Department

д-р. техн. наук, профессор, зав. кафедрой «Высшая математика и физика»

poleshchuk@mgul.ac.ruKomarovEvgeniy G.КомаровЕвгений Геннадиевич
Russian Federation

Dr. Sci. (Tech.), Professor, Head of the Department of Information and Measuring Systems and Instrumentation Technologies

д-р. техн. наук, профессор, зав. кафедрой «Информационно-измерительные системы и технологии приборостроения»

komarov@mgul.ac.ruPoyarkovNikolay G.ПоярковНиколай Геннадьевич
Russian Federation

Cand. Sci. (Tech.), Associate Professor, Dean of the Space Faculty

канд. техн. наук, доцент, декан Космического факультета

poyarkov@mgul.ac.ruBMSTU (Mytishchi branch)ФГБОУ ВО «Московский государственный технический университет имени Н.Э. Баумана (национальный исследовательский университет)»290620242831331402904202629042026Copyright ©; 2024, Poleshchuk O.M., Komarov E.G., Poyarkov N.G.Copyright ©; 2024, Полещук О.М., Комаров Е.Г., Поярков Н.Г.2024Poleshchuk O.M., Komarov E.G., Poyarkov N.G.Полещук О.М., Комаров Е.Г., Поярков Н.Г.https://creativecommons.org/licenses/by/4.0https://journals.eco-vector.com/2542-1468/article/view/706981

Quality indicators of fuzzy regression models designed to study the dependencies between the qualitative characteristics of the educational process and to predict their values, as well as a model for recognizing fuzzy values of the output characteristics of regressions are presented. An algorithm for selecting a fuzzy regression model from linear and nonlinear models based on their quality indicators is given. An analysis of the degree of influence of input characteristics on the output characteristic is carried out. A fuzzy regression model has been constructed to predict the success of the dissertation defense when the applicant enters the PhD program and to study the dependencies between the applicant’s input characteristics and the output characteristic. An alternative approach to the construction of regression models based on non-numerical data of the educational process allows not to impose incorrect conditions on the initial data, considering them to be the values of random variables, and not to use incorrect arithmetic operations for the elements of ordinal scales.

Представлены показатели качества нечетких регрессионных моделей, предназначенных для исследования зависимостей между качественными характеристиками образовательного процесса и для прогноза их значений, а также модель распознавания нечетких значений выходных характеристик регрессий. Приведен алгоритм выбора нечеткой регрессионной модели из линейной и нелинейной моделей на основе показателей их качества. Проведен анализ степеней влияния входных характеристик на выходную характеристику. Построена нечеткая регрессионная модель для прогноза успешности защиты диссертации при поступлении соискателя в аспирантуру и для исследования зависимостей между входными характеристиками соискателя и выходной характеристикой. Альтернативный подход к построению регрессионных моделей на основе нечисловых данных образовательного процесса позволяет не накладывать некорректные условия на исходные данные, считая их значениями случайных величин, и не использовать некорректные арифметические операции для элементов порядковых шкал.

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