The search result changed since you submitted your search request. Documents might be displayed in a different sort order.
  • search hit 9 of 528
Back to Result List

AI case-based reasoning for artificial neural networks

  • Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the mostFaced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.show moreshow less

Export metadata

Additional Services

Search Google Scholar Statistics
Metadaten
Author details:Marcus GrumORCiDGND, Christof ThimORCiDGND, Wiebke RolingORCiDGND, Arnulf SchüfflerORCiDGND, Annette KlugeORCiDGND, Norbert GronauORCiDGND
DOI:https://doi.org/10.1007/978-3-031-43524-9_2
ISBN:978-3-031-43523-2
ISBN:978-3-031-43524-9
Title of parent work (English):Artificial intelligence and industrial applications
Publisher:Springer
Place of publishing:Cham
Editor(s):Tawfik Masrour, Ibtissam El Hassani, Noureddine Barka
Publication type:Conference Proceeding
Language:English
Date of first publication:2023/09/15
Publication year:2023
Release date:2024/03/07
Tag:case-based reasoning; industry 4.0; neural networks
Volume:771
Number of pages:19
First page:17
Last Page:35
Organizational units:Wirtschafts- und Sozialwissenschaftliche Fakultät / Wirtschaftswissenschaften / Fachgruppe Betriebswirtschaftslehre
DDC classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Peer review:Nicht ermittelbar
Accept ✔
This website uses technically necessary session cookies. By continuing to use the website, you agree to this. You can find our privacy policy here.