Predictive associative models of processes and situations. Issue 2 (2022)

• Record Type: Journal Article
• Authors: Bakhtadze, N.; Chereshko, A.; Elpashev, D.; Suleykin, A.; Purtov, A.
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 2(2022)
• Page Start: 19
• Page End: 24
• Publication Date: 2022
• Subjects: nonlinear process -- control systems for nonlinear -- time-varying objects -- knowledgebase -- associative search models -- quantum clustering; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.04.163
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The paper offers identification methods for production processes whose dynamics undergoes heterogeneous factors. The methods are based on the analysis of the accumulated knowledge about the process. Linear discrete predictive models of production situations are created for digital twins. When developing models, the methods of associative search and clustering are used. The possibility of using quantum clustering for the proposed identification methods in case of big data processing is examined. Case studies are included.