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Keynote Lecture


Evolving Systems and Their Automotive Applications

Dimitar Filev
Research & Advanced Engineering, Ford Motor Company
United States

Brief Bio
Dr. Dimitar Filev is a Henry Ford Technical Fellow at the Ford Research & Innovation Center, Dearborn, Michigan. He is conducting research in computational intelligence, AI and control, and their applications to vehicle systems, autonomous driving, and automotive engineering.  Dr. Filev has published 4 books, over 200 journal articles and conference papers, and holds over 100 US and foreign patents.  He is the recipient of the 2008 Norbert Wiener Award of the IEEE SMC Society and the 2015 Pioneer’s Award of the IEEE CIS Society. He received his PhD. degree in Electrical Engineering from the Czech Technical University in Prague in 1979.  Dr. Filev is a Fellow of IEEE and a member of the NAE. He is past president of the IEEE Systems, Man, & Cybernetics Society (2016-2017).

The emerging trend of increasing flexibility, adaptation, and autonomy of control and information systems is the driving force behind the evolving systems paradigm. Evolving systems are characterized with flexible model structure that adjusts to changes which cannot be solely handled by parameter adaptation. Evolving systems develop their structure and knowledge representation through continuous learning from data and interaction with the environment. They exploit synergies between two powerful concepts – real time data granulation and machine learning - with no limitations on the types of the model structure that may include regression models, neural networks, fuzzy, and/or stochastic models. Practical applications encompass a wide range of systems with variable parameters and structure, and multiple operating modes. This presentation provides an overview of the multiple facets of evolving systems theory and describes some of their automotive applications to adaptive process control, automated calibration, anomaly detection, driver state estimation, and fuel economy optimization.