System Identification with Quantized Observations [electronic resource] /
Wang, Le Yi.author.
Yin, G. George.author.
Zhang, Ji-Feng.author.
Zhao, Yanlong.author.
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.
Description based upon print version of record.
Includes bibliographical references ([305]-314) and index.
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.
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.
System theory.
Mathematical models.
Control engineering.
Algorithms.
Electrical engineering.
Probabilities.
Systems Theory, Control.
Mathematical Modeling and Industrial Mathematics.
Control and Systems Theory.
Algorithms.
Communications Engineering, Networks.
Probability Theory and Stochastic Processes.