System Identification With Quantized Observations
Language: English
Published by Birkhäuser, 2010
Series: Book 28 of 55 - Systems & Control: Foundations & Applications
- Hardcover
- Used
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- Title
- System Identification With Quantized Observations
- Author
- Wang, Le Yi; Yin, G. George; Zhang, Ji-Feng; Zhao, Yanlong
- Publisher
- Birkhäuser
- Publication year
- 2010
- Condition
- As New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0817649557
- ISBN 13
- 9780817649555
- Series
- Book 28 of 55: Systems & Control: Foundations & Applications
This book presents recently developed methodologies that utilize quantized information in system identification and explores their potential in extending control capabilities for systems with limited sensor information or networked systems. The results of these methodologies can be applied to signal processing and control design of communication and computer networks, sensor networks, mobile agents, coordinated data fusion, remote sensing, telemedicine, and other fields in which noise-corrupted quantized data need to be processed.
System Identification with Quantized Observations is an excellent resource for graduate students, systems theorists, control engineers, applied mathematicians, as well as practitioners who use identification algorithms in their work.
"Synopsis" may belong to another edition of this title.
From the Back Cover
This book presents recently developed methodologies that utilize quantized information in system identification and explores their potential in extending control capabilities for systems with limited sensor information or networked systems. The results of these methodologies can be applied to signal processing and control design of communication and computer networks, sensor networks, mobile agents, coordinated data fusion, remote sensing, telemedicine, and other fields in which noise-corrupted quantized data need to be processed.
Providing a comprehensive coverage of quantized identification, the book treats linear and nonlinear systems, as well as time-invariant and time-varying systems. The authors examine independent and dependent noises, stochastic- and deterministic-bounded noises, and also noises with unknown distribution functions. The key methodologies combine empirical measures and information-theoretic approaches to derive identification algorithms, provide convergence and convergence speed, establish efficiency of estimation, and explore input design, threshold selection and adaptation, and complexity analysis.
System Identification with Quantized Observations is an excellent resource for graduate students, systems theorists, control engineers, applied mathematicians, as well as practitioners who use identification algorithms in their work. Selected material from the book may be used in graduate-level courses on system identification.
"About the title" may belong to another edition of this title.
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