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Data-Driven Modeling, Filtering and Control: Methods and applications

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Editors: Carlo Novara 1 ; Simone Formentin 2
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Publication Year: 2019

The scientific research in many engineering fields has been shifting from traditional first-principle-based to data-driven or evidence-based theories. The latter methods may enable better system design, based on more accurate and verifiable information. In the era of big data, IoT and cyber-physical systems, this subject is of growing importance, as data-driven approaches are key enablers to solve problems that could not be addressed by standard approaches. This book presents a number of innovative data-driven methodologies, complemented by significant application examples, to show the potential offered by the most recent advances in the field. Applicable across a range of disciplines, the topics discussed here will be of interest to scientists, engineers and students in automatic control and learning systems, automotive and aerospace engineering, electrical engineering and signal processing.

Inspec keywords: filtering theory; control systems; identification

Other keywords: data-driven filtering; IoT; big data; Internet of Things; system identification; cyber-physical systems; data-driven modeling; data-driven control

Subjects: Signal processing theory; General and management topics; Simulation, modelling and identification; Specific control systems

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