Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Ensuring Functional Stability of Technological Processes as Cyberphysical Systems Using Neural Networks
https://orcid.org/http://orcid.org/0000-0002-4002-8206
https://orcid.org/http://orcid.org/0000-0001-5681-1871
https://orcid.org/http://orcid.org/0000-0002-7291-1829
Complex technical systems form multilevel structures and are built to perform special tasks. By analogy with natural systems, the development of such systems leads to the complication of their functioning and the emergence of new properties, such as, in fact, functional stability. Widespread use of artificial intelligence in technological processes as cyber-physical systems allows for management based on fundamental approaches to the stability of complex systems. Depending on the complexity of the organization of enterprise information systems and the level of analysis, the property of functional stability can be manifested in the form of resistance to errors, reliability, survivability, fault tolerance, adaptability, noise immunity and more. The characteristics that ensure the functional stability of technological systems and control of dynamic processes of production processes of metalworking in machine-building enterprises on the basis of nonlinear dynamics, fractal analysis and artificial intelligence are studied. Taking into account the peculiarities of metal cutting processes, the universal construction of neural network models of the machining process based on an artificial counter-neural neural network is proposed and substantiated. To ensure the functional stability of the production process at machine-building enterprises and enterprises of the mining and metallurgical complex, an intelligent system of analysis and forecasting of the dynamic stability of the technological process of cutting with the help of parallel calculations is proposed.
Ensuring Functional Stability of Technological Processes as Cyberphysical Systems Using Neural Networks
https://orcid.org/http://orcid.org/0000-0002-4002-8206
https://orcid.org/http://orcid.org/0000-0001-5681-1871
https://orcid.org/http://orcid.org/0000-0002-7291-1829
Complex technical systems form multilevel structures and are built to perform special tasks. By analogy with natural systems, the development of such systems leads to the complication of their functioning and the emergence of new properties, such as, in fact, functional stability. Widespread use of artificial intelligence in technological processes as cyber-physical systems allows for management based on fundamental approaches to the stability of complex systems. Depending on the complexity of the organization of enterprise information systems and the level of analysis, the property of functional stability can be manifested in the form of resistance to errors, reliability, survivability, fault tolerance, adaptability, noise immunity and more. The characteristics that ensure the functional stability of technological systems and control of dynamic processes of production processes of metalworking in machine-building enterprises on the basis of nonlinear dynamics, fractal analysis and artificial intelligence are studied. Taking into account the peculiarities of metal cutting processes, the universal construction of neural network models of the machining process based on an artificial counter-neural neural network is proposed and substantiated. To ensure the functional stability of the production process at machine-building enterprises and enterprises of the mining and metallurgical complex, an intelligent system of analysis and forecasting of the dynamic stability of the technological process of cutting with the help of parallel calculations is proposed.
Ensuring Functional Stability of Technological Processes as Cyberphysical Systems Using Neural Networks
https://orcid.org/http://orcid.org/0000-0002-4002-8206
https://orcid.org/http://orcid.org/0000-0001-5681-1871
https://orcid.org/http://orcid.org/0000-0002-7291-1829
Complex technical systems form multilevel structures and are built to perform special tasks. By analogy with natural systems, the development of such systems leads to the complication of their functioning and the emergence of new properties, such as, in fact, functional stability. Widespread use of artificial intelligence in technological processes as cyber-physical systems allows for management based on fundamental approaches to the stability of complex systems. Depending on the complexity of the organization of enterprise information systems and the level of analysis, the property of functional stability can be manifested in the form of resistance to errors, reliability, survivability, fault tolerance, adaptability, noise immunity and more. The characteristics that ensure the functional stability of technological systems and control of dynamic processes of production processes of metalworking in machine-building enterprises on the basis of nonlinear dynamics, fractal analysis and artificial intelligence are studied. Taking into account the peculiarities of metal cutting processes, the universal construction of neural network models of the machining process based on an artificial counter-neural neural network is proposed and substantiated. To ensure the functional stability of the production process at machine-building enterprises and enterprises of the mining and metallurgical complex, an intelligent system of analysis and forecasting of the dynamic stability of the technological process of cutting with the help of parallel calculations is proposed.
Ensuring Functional Stability of Technological Processes as Cyberphysical Systems Using Neural Networks
Lect. Notes in Networks, Syst.
Arsenyeva, Olga (Herausgeber:in) / Romanova, Tatiana (Herausgeber:in) / Sukhonos, Maria (Herausgeber:in) / Tsegelnyk, Yevgen (Herausgeber:in) / Sobchuk, Valentyn (Autor:in) / Zamrii, Iryna (Autor:in) / Laptiev, Serhii (Autor:in)
International Conference on Smart Technologies in Urban Engineering ; 2022 ; Kharkiv, Ukraine
29.11.2022
12 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
An Adaptive Energy Orchestrator for Cyberphysical Systems Using Multiagent Reinforcement Learning
| DOAJ | 2024