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Adaptive activity driven multi-level hierarchical prediction of complex systems through profiling and feedback

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Résumé

Complex systems composed of multi-level hierarchical systems exhibit complex interacting dependences which affects the performance of the overall system. Although, one solution is to tightly monitor each system activity and feedback to the next step level in order to take appropriate measures and actions to improve the system this solution is costly and too systematic. In addition, a dependency chain exists between low level systems and higher level systems this dependency chains being as long as the number of levels of the system. We propose in this paper an activity driven adaptive hierarchical prediction technique for complex systems which minimizes monitoring and prediction resources requirements and still keep efficient overall systems performance prediction.

langue originaleAnglais
titre19th Annual International Symposium of the International Council on Systems Engineering (INCOSE 2009) in conjunction with the 3rd Asia-Pacific Conference on Systems Engineering APCOSE 2009
EditeurINCOSE-International Council on Systems Engineering
Pages1663-1675
Nombre de pages13
ISBN (imprimé)9781615674398
Les DOIs
étatPublié - 1 janv. 2009
Evénement19th Annual International Symposium of the International Council on Systems Engineering, INCOSE 2009 - , Singapour
Durée: 20 juil. 200923 juil. 2009

Série de publications

Nom19th Annual International Symposium of the International Council on Systems Engineering, INCOSE 2009
Volume3

Une conférence

Une conférence19th Annual International Symposium of the International Council on Systems Engineering, INCOSE 2009
Pays/TerritoireSingapour
période20/07/0923/07/09

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