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Stokastiska processer, statistik och finansmatematik

Säkerhetskritisk teknik

Teknisk matematik

Vård, återhämtning och hälsa

Programmeringsspråk

Programvarutestlaboratorium

Resurseffektivisering

Artificiell intelligens och intelligenta system

Automatiserade mjukvaruspråkutveckling och mjukvaruteknik

Certifierbara bevis och justifieringsteknik

ChiP – Barns rätt till hälsa, skydd, stöd och delaktighet

Cyber-fysisk systemanalys

Datakommunikation

Digitalisering

Formell modellering och analys av inbyggda system

Förnybar energi

Heterogena system

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

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

Forskningsinriktning

Projektansvarig vid MDH

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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.

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