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

Research in the field of system identification and control has been shifting from traditional model-based to data-driven or evidence-based theories. The latter methods enable better designs based on more direct and accurate data-based information and verifiable data. In the era of big data, IoT, and...

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Bibliographic Details
Corporate Author: Institution of Engineering and Technology
Other Authors: Formentin, Simone (Editor), Novara, Carlo (Editor)
Format: eBook
Language:English
Published: Stevenage : IET, 2019.
Series:Control, Robotics & Sensors.
Subjects:
Filters (Mathematics)
Identification.
control systems.
filtering theory.
identification.
Internet of Things
IoT
big data
cyber-physical systems
data-driven control
data-driven filtering
data-driven modeling
system identification
Online Access:Connect to the full text of this electronic book
Description
Summary:Research in the field of system identification and control has been shifting from traditional model-based to data-driven or evidence-based theories. The latter methods enable better designs based on more direct and accurate data-based information and verifiable data. 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 previous 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.
Physical Description:1 online resource (301 pages)
ISBN:9781785617133
DOI:10.1049/PBCE123E