Buch, Englisch, Band 450, 306 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 5444 g
Buch, Englisch, Band 450, 306 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 5444 g
Reihe: Lecture Notes in Control and Information Sciences
ISBN: 978-3-319-02149-2
Verlag: Springer International Publishing
· data-rate theorems;
· computation and control over communication networks;
· decentralized stochastic control;
· Gaussian networks and Gaussian–Markov random fields; and
· routability in information networks.
Information and Control in Networks collects contributions from world-leading researchers in the area who came together for the Lund Center for Control of Complex Engineering Systems Workshop in Information and Control in Networks from 17th–19th October 2012; the workshop being the centrepiece of a five-week-long focus period on the same theme. A source of exciting cross-fertilization and new ideas for extensive future research, this volume will be of great interest to any researcher or graduate student interested in the interaction of control and information theory.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Informationstheorie, Kodierungstheorie
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
Weitere Infos & Material
Elements of Information Theory for Networked Control Systems.- Stabilization and Control over Gaussian Networks.- Optimal Radio-Mode Switching for Wireless Networked Control.- The Common-Information Approach to Decentralized Stochastic Control.- Relations between Information and Estimation in the presence of Feedback.- Design of Information Channels for Optimization and Stabilization in Networked Control.- Structural Routability of n-Pairs Information Networks.- Computing over unreliable communication networks.- On the Conditional Mutual Information in the Gaussian-Markov Structured Grids.