System Identification with Quantized Observations

• 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.

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)