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Artificiell intelligens och intelligenta system

Learning, Inclusive education, School transitions – for All (LISA)

Lärande och optimering

M-TERM - Mälardalen University Team of Educational Researchers in Mathematics

Mälardalen INteraction and Didactics (MIND)

NMP – nya managementpraktiker

Personcentrerad vård och kommunikation

Simulering och optimering för framtida industriella applikationer

Stokastiska processer, statistik och finansmatematik

Teknisk matematik

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

Projektansvarig vid MDH

Professor i artificiell intelligens/lärande system

Ning Xiong

+4621151716

ning.xiong@mdh.se

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.