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230515 ||| eng |
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|a books978-3-0365-6809-6
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|a 9783036568096
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|a 9783036568089
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|a Carpanzano, Emanuele
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| 245 |
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|a Advances in Artificial Intelligence Methods Applications in Industrial Control Systems
|h Elektronische Ressource
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| 260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2023
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| 300 |
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|a 1 electronic resource (150 p.)
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| 653 |
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|a machine learning
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| 653 |
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|a self-learning machine tools
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| 653 |
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|a robotic grasping
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| 653 |
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|a multi-agent
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| 653 |
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|a uncertain nonlinear system
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| 653 |
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|a n/a
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| 653 |
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|a reconfiguration
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| 653 |
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|a predictive control
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| 653 |
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|a OPC UA
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| 653 |
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|a predictive model
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| 653 |
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|a adaptive production systems
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| 653 |
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|a digital twin
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| 653 |
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|a continuous control
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| 653 |
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|a History of engineering & technology / bicssc
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| 653 |
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|a irrigation main canal pool automation
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| 653 |
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|a modified SP controller design
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| 653 |
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|a Technology: general issues / bicssc
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| 653 |
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|a control systems
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| 653 |
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|a policy iteration
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| 653 |
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|a cyber-physical production system
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| 653 |
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|a manufacturing execution system
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| 653 |
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|a reinforcement learning
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| 653 |
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|a robust control
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| 653 |
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|a adaptive optimal control
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| 653 |
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|a management of water resources
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| 653 |
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|a SUGV
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| 653 |
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|a decentralized control
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| 653 |
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|a nonlinear system
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| 653 |
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|a artificial intelligence
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| 653 |
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|a interoperability
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| 653 |
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|a industrial automation
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| 653 |
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|a system identification
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| 653 |
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|a industry 4.0
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| 653 |
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|a NARX-ANN-based models
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| 653 |
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|a multi-dimensional Taylor network
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| 653 |
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|a policy optimization
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| 653 |
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|a human robot collaboration
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| 700 |
1 |
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|a Carpanzano, Emanuele
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b DOAB
|a Directory of Open Access Books
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| 500 |
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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| 028 |
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|a 10.3390/books978-3-0365-6809-6
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| 856 |
4 |
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|u https://directory.doabooks.org/handle/20.500.12854/98920
|z DOAB: description of the publication
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| 856 |
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|u https://www.mdpi.com/books/pdfview/book/6973
|7 0
|x Verlag
|3 Volltext
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| 082 |
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|a 900
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|a 140
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|a 700
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|a 330
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|a The motivation for the present reprint is to provide an overview of novel applications of AI methods to industrial control systems by means of selected best practices in highlighting how such methodologies can be used to improve the production systems self-learning capacities, their overall performance, the related process and product quality, the optimal use of resources and the industrial systems safety, and resilience to varying boundary conditions and production requests. By means of its seven scientific contributions, the present reprint illustrates the increasing added value of the introduction of AI methods for improving the performance of control solutions with reference to different control and automation problems in different industrial applications and sectors, ranging from single manipulators or small unmanned ground vehicles up to complex manufacturing. Additionally, the role of AI to improve the performance of relevant engineering methodologies and digital instruments, such as cyberphysical systems, digital twins, and human-robot collaboration, are also effectively addressed in the included contributions.
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