Keynote Lecturer: Edwin Lughofer
The keynote speech will provide a round picture of the developments and recent advances in the field of evolving fuzzy systems (EFS) achieved during the last decade since their first time appearance at the beginning of this century.Opposed to conventional fuzzy systems, EFS can be learnt from data (streams) on the fly during (fast) on-line processes in an incremental and mostly single-pass manner. They enjoy a flexible model structure that is able to automatically self-evolve and self-adapt to changes in the process, as e.g. caused by system drifts, new operation modes or dynamic environmental conditions. Therefore, they stand for a very emerging topic in the field of soft computing to address modeling problems in nowadays real-world applications with quickly increasing complexity, more and more implying a shift from batch off-line model design phases (as conducted since the 80ties) to permanent on-line (active) model teaching and adaptation cycles. Furthermore, they can be used in the context of on-line data stream mining and incremental extraction of models and knowledge from huge data bases, not being able to be loaded into virtual memory at once. The focus will be placed on the definition of various model architectures used in the context of EFS, providing an overview about the basic learning concepts and listing the most prominent EFS approaches (fundamentals), discussing recent advances towards an improved stability, reliability and useability (guaranteeing robustness and userfriendliness) as well as aspects towards a grown-up interpretability (offering insights into systems‘ characteristics and nature). The speech will be concluded with a summary of real-world applications such as on-line condition monitoring, visual inspection, human-machine interaction, smart sensors, production systems and others, where various EFS approaches have been successfully applied.