Surviving the Data Deluge: A Combined Dynamical Systems/Machine Learning Approach
Technical Report,01 Sep 2015,31 Aug 2019
NORTHEASTERN UNIVERSITY Boston United States
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This research sought to develop a comprehensive, computationally tractable framework for synthesizing information driven systems capable of both autonomously operating and supporting safety--critical human operations in rapidly changing data deluged scenarios. Its conceptual backbone was a rigorous integration of systems theory, machine learning and optimization elements that emphasized robustness, computational simplicity and improved situational awareness. The research advanced the state of the art in systems theory by developing a tractable framework for robust identificationmodel invalidation of a broad class of dynamical systems that incorporates ideas from machine learning and semi-algebraic optimization to handle outliers, missing data and substantial noise levels.
- Human Factors Engineering and Man Machine Systems
- Information Science