System Identification with Quantized Observations (inbunden)
Inbunden (Hardback)
Antal sidor
Birkhauser Boston Inc
Yin, G. George / Zhang, Ji-Feng
40 schwarz-weiße Abbildungen
3 Tables, black and white; 42 Illustrations, black and white; XVIII, 317 p. 42 illus.
234 x 158 x 25 mm
635 g
Antal komponenter
1 Hardback
System Identification with Quantized Observations (inbunden)

System Identification with Quantized Observations

Inbunden Engelska, 2010-03-01
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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.
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From the reviews: "The central idea in this book is to provide a comprehensive treatment of both theory and algorithms needed for parameter identification of systems with quantized observations. ... the book conveys a clear and very complete overview of recent exciting developments in the area of identification with quantized observations. It is meant as a 'state-of-the-art' book ... . All this makes the book an extremely valuable resource for researchers and engineers interested in modern system identification." (Dariusz Ucinski, Mathematical Reviews, Issue 2011 i)

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Overview.- System Settings.- Stochastic Methods for Linear Systems.- Empirical-Measure-Based Identification: Binary-Valued Observations.- Estimation Error Bounds: Including Unmodeled Dynamics.- Rational Systems.- Quantized Identification and Asymptotic Efficiency.- Input Design for Identification in Connected Systems.- Identification of Sensor Thresholds and Noise Distribution Functions.- Deterministic Methods for Linear Systems.- Worst-Case Identification under Binary-Valued Observations.- Worst-Case Identification Using Quantized Observations.- Identification of Nonlinear and Switching Systems.- Identification of Wiener Systems with Binary-Valued Observations.- Identification of Hammerstein Systems with Quantized Observations.- Systems with Markovian Parameters.- Complexity Analysis.- Space and Time Complexities, Threshold Selection, Adaptation.- Impact of Communication Channels on System Identification.