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Artificiell intelligens och intelligenta system
Automatiserade mjukvaruspråkutveckling och mjukvaruteknik
Digitalisering
Formell modellering och analys av inbyggda system
Förnybar energi
Hållbar livsstil och hälsa ut ett folkhälsoperspektiv
Industriell programvaruteknik
Komplexa inbyggda system i realtid
Learning, Inclusive education, School transitions – for All (LISA)
Lärande och optimering
Medicinsk teknik
Modellbaserad konstruktion av inbäddade system
M-TERM - Mälardalen University Team of Educational Researchers in Mathematics
Forskargruppen MIND (Mälardalen INteraction and Didactics)
Personcentrerad vård och kommunikation
Programmeringsspråk
Certifierbara bevis och justifieringsteknik
Cyber-fysisk systemanalys
Datakommunikation
ChiP – Barns rätt till hälsa, skydd, stöd och delaktighet
Heterogena system
Programvarutestlaboratorium
Resurseffektivisering
Stokastiska processer, statistik och finansmatematik
Säkerhetskritisk teknik
Teknisk matematik
Vård, återhämtning och hälsa
Computational Intelligence in Process Modelling and Prediction
This project aims to exploit a hybrid approach using learning techniques based on computational intelligence to build knowledge-based models and associated reasoning mechanisms for process modeling, prediction and classification.
Avslutat
Start
2012-01-01
Avslut
2013-12-31
Forskningsområde
Forskningsinriktning
Projektansvarig vid MDH
Description of the project
Process modeling and prediction presents a crucial issue to develop adaptive strategies in coping with industrial manufacturing and production lines. However, complex processes in industry are often hard to model using conventional mathematical techniques and algorithms on their own.
This project aims to exploit a hybrid approach using learning techniques based on computational intelligence to build knowledge-based models and associated reasoning mechanisms for process modeling, prediction and classification.
The key techniques employed in the research will include: fuzzy computing, case-based reasoning, nature-inspired optimization, and perhaps also probabilistic inference to accommodate stochastic property of processes.