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

Mälardalen Interaction and Didactics (MIND)

Artificial Intelligence och Intelligent Systems

Simulation and optimisation for future industrial applications (SOFIA)

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.

Concluded

Start

2012-01-01

Conclusion

2013-12-31

Project manager at MDH

Professor

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.