Items related to System Identification with Quantized Observations
Synopsis
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.
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Review
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 Uciński, Mathematical Reviews, Issue 2011 i)"About this title" may belong to another edition of this title.
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