Definitions dynamic properties of virtual objects
Микола Степанович Жуков
Krivyi Rih Metallurgical Institute of the National Metallurgical Academy of Ukraine
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Keywords

virtual object
transitional functions
dynamic links of ACS
identification parameters
least squares method
discrete model
difference equations
MATLAB

How to Cite

Жуков, М. (2017). Definitions dynamic properties of virtual objects. New Computer Technology, 15, 73-79. https://doi.org/10.55056/nocote.v15i0.607
PDF (Українська)

Abstract

The aim of the study is to create a tools for training in determination of dynamic properties of objects. The research tasks is the development of tools for workshops in “Identification and modeling of technological processes”. The research object is to determine dynamic properties of objects that are characterized by differential equations or transfer functions. The research subject is to generate and use arrays of pseudo experimental data in the workshops. It was considered to develop tools for practical lessons on discipline «Identification and modeling of technological processes» that provide working conditions close to reality. The goal achieved by the establishment of data that mimic the behavior of a real object or process in time and proper identification procedures carried out independently. Arrays of these virtual objects created using the transition functions typical dynamic links of ACS and possible obstacles and write into files. This increases the anonymity of sources pseudo experimental data. To identify the parameters proposed algorithm minimizing the quadratic criterion rejection observations that the scanned within а file, and approximating model. Used a discrete object model in the form of difference equations. Creating a pseudo experimental data and further studies are performed by software package MATLAB. The text has fragments of program explaining basic details of implementation. The development is designed for active assimilation method of identification in the form of training as an effective learning. The focus of this method is not only to obtain theoretical knowledge, but also through training receive skills.

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References

1. Линник Ю. В. Метод наименьших квадратов и основы теории обработки наблюдений. Изд. 2-е, доп. и испр. – М. : Государственное издательство физико-математической литературы, 1962. – 352 с.
2. Льюнг Л. Идентификация систем. Теория для пользователя : пер. с англ. / Под ред. Я. З. Цыпкина. – М. : Наука. Гл. ред. физ.-мат. лит., 1991. – 432 с.
3. Гутер Р. С. Элементы численного анализа и математической обработки результатов опыта / Р. С. Гутер, Б. В. Овчинский. – М. : Наука. Гл. ред. физ.-мат. лит., 1970. – 432 с.
4. Методы классической и современной теории автоматического управления : учебник в 5-ти т.; 2-е изд., перераб. и доп. Т. 2 : Статистическая динамика и идентификация систем автоматического управления / под ред. К. А. Пупкова и Н. Д. Егупова. – М. : Издательство МГТУ им. Н. Э. Баумана, 2004. – 640 с.
5. Наместников С. М. Основы программирования в MatLab : сборник лекций. – Ульяновск: УлГТУ, 2011. – 55 с.
6. Гультяев А. К. MATLAB 5.3. Имитационное моделирование в среде Windows: Практическое пособие. – СПб. : Корона принт, 2001. – 400 с.
7. Бесекерский В. А. Теория систем автоматического управления / В. А. Бесекерский, Е. П. Попов. – Изд. 4-е перераб. и доп. – СПб. : Профессия, 2003. – 752 с.