Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industri...

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Veröffentlicht in:Electronics (Basel) Jg. 12; H. 1; S. 217
Hauptverfasser: Rajawat, Anand Singh, Goyal, S. B., Chauhan, Chetan, Bedi, Pradeep, Prasad, Mukesh, Jan, Tony
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.01.2023
Schlagworte:
Adaptive systems
Algorithms
Automation
Cognitive ability
Computers
Datasets
Deep learning
Factories
Industrial applications
Industrial Internet of Things
Intelligence
Machine learning
Manufacturing
Product development
Reinforcement learning (Machine learning)
Semantics
India
ISSN:2079-9292, 2079-9292
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Zusammenfassung:Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industrial production systems to be flexible and resilient to respond to changes in real time. This study examines the use of RL in a wide range of adaptive cognitive systems with IIoT-edges in manufacturing processes. We propose a cognitive adaptive system using IIoT with RL (CAS-IIoT-RL) and our experimental analysis showed that the proposed model showed improvements with adaptive and dynamic decision controls in challenging industrial environments.
Bibliographie:ObjectType-Article-1
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12010217