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Traektoriâ Nauki
Article . 2023 . Peer-reviewed
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Design of Learning Models and Algorithmic Approaches in an Expert Learning System

Authors: Alizade, Aynur Akhmad kizi;

Design of Learning Models and Algorithmic Approaches in an Expert Learning System

Abstract

Статья раскрывает структуры моделей обучения в экспертной системе обучения и принципы алгоритмического построения при проектировании данной системы. Проектирование и роль моделей обучения в процессе обучения экспертной системы, а также необходимость соблюдения единых принципов алгоритмического построения при проектировании данных моделей, детально излагаются в ниже написанном контексте. В представленной статье, предлагаемые типы моделей формируются в аналитическом процессоре, являющимся межбазовым звеном, регулирующим процесс обучения. В процессе обучения необходимы различные виды обучаемых моделей, в зависимости от функции, которые они выполняют.

The article reveals the structures of learning models in an expert learning system and the principles of algorithmic construction in the design of this system. The invention and role of learning models in learning an expert system and the need to comply with the unified principles of algorithmic construction when designing these models are described in detail in the context written below. In the presented article, the proposed types of models are formed in the analytical processor, which is an InterBase link that regulates the learning process. Depending on their function, different types of trainable models are needed in the learning process.

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Keywords

LСC Subject Category: PE1001-1693, Languages, экспертная система обучения; модели обучения; диагностическое тестирование; имитационные модели; алгоритмический подход; оверлейная модель; искусственный интеллект; карманный словарь, expert learning system; learning models; diagnostic testing; simulation models; algorithmic approach; overlay model; artificial intelligence; pocket dictionary

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold