59829

Автор(ы): 

Автор(ов): 

2

Параметры публикации

Тип публикации: 

Статья в журнале/сборнике

Название: 

Industrial digital ecosystems: Predictive models and architecture development issues

ISBN/ISSN: 

1367-5788

DOI: 

10.1016/j.arcontrol.2020.11.001

Наименование источника: 

  • Annual Reviews in Control

Обозначение и номер тома: 

51

Город: 

  • Cham

Издательство: 

  • Elsevier

Год издания: 

2021

Страницы: 

56-64, https://www.sciencedirect.com/science/article/pii/S1367578820300766
Аннотация
The concept of digital ecosystem (DES) is widely used in autonomous manufacturing process control and the development of complex, stable, interactive, self-organizing and reliable management systems for various industries. The paper offers a concept of DES control system architecture based on predictive models. For estimating and predicting the state of resources in production processes, an approach is developed using data mining based model generation. The paper also reviews the current state of research in the field of DES and their applications in supply chain management (SCM).

Библиографическая ссылка: 

Бахтадзе Н.Н., Сулейкин А.С. Industrial digital ecosystems: Predictive models and architecture development issues // Annual Reviews in Control. 2021. 51. С. 56-64, https://www.sciencedirect.com/science/article/pii/S1367578820300766.