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Other titles in the Adaptive and Learning Systems for Signal Processing, Communications and Control series:

Control Oriented System Identification

Control Oriented System Identification Cover

 

Synopses & Reviews

Publisher Comments:

A comprehensive, one-stop reference for new system modeling and identification tools

The field of control-oriented identification has grown immensely over the past decade, spawning numerous results and modeling techniques and promising the potential to influence science and engineering for years to come. In this new work, Jie Chen and Guoxiang Gu, two leading authorities on worst-case identification, share their vision and walk readers through carefully selected topics from the vast literature, offering a much-needed, timely comprehensive introduction to the theory of Hâ identification and model validation.

Chen and Gu clearly demonstrate the pros and cons of the worst-case approach in comparison to traditional techniques and provide researchers in systems and control theory with ready access to many new and complementary identification tools. Through a rigorous yet logical and easy-to-follow treatment, supported by many deep insights, intuitions, and philosophical thinking, they:

  • Survey and assess the current state of control and system identification research
  • Develop both two-stage and interpolatory algorithms for system identification
  • Show readers how to analyze the properties of linear algorithms
  • Offer a unique emphasis on model uncertainty estimation and complexity, two of the central issues
  • Develop both time-domain and frequency-domain identification algorithms
  • Explain in detail uncertainty model validation concepts and techniques
  • Devote a chapter to a review of the requisite mathematics

Provide a concise yet self-contained appendix on several key relevant notions

Book News Annotation:

Writing for researchers and advanced graduate students, electrical and computer engineers Chen (U. of California, Riverside) and Gu (Louisiana State U., Baton Rouge) offer a broad and detailed view of the field of robust control-oriented identification theory, as this field has developed since the 1980s. Following an introduction, the authors devote two-thirds of the book to a detailed exposition of the mathematics, covering function spaces and signals, harmonic analysis, analytic function approximation, H-[infinity] control and identification, and linear, nonlinear, and time domain algorithms. The remainder of the volume teaches the principles, techniques, and potential pitfalls of model validation problem formulation.
Annotation c. Book News, Inc., Portland, OR (booknews.com)

Synopsis:

Provide a concise yet self-contained appendix on several key relevant notions

Synopsis:

A comprehensive, one-stop reference for new system modeling and identification tools

The field of control-oriented identification has grown immensely over the past decade, spawning numerous results and modeling techniques and promising the potential to influence science and engineering for years to come. In this new work, Jie Chen and Guoxiang Gu, two leading authorities on worst-case identification, share their vision and walk readers through carefully selected topics from the vast literature, offering a much-needed, timely comprehensive introduction to the theory of H? identification and model validation.

Chen and Gu clearly demonstrate the pros and cons of the worst-case approach in comparison to traditional techniques and provide researchers in systems and control theory with ready access to many new and complementary identification tools. Through a rigorous yet logical and easy-to-follow treatment, supported by many deep insights, intuitions, and philosophical thinking, they:

  • Survey and assess the current state of control and system identification research
  • Develop both two-stage and interpolatory algorithms for system identification
  • Show readers how to analyze the properties of linear algorithms
  • Offer a unique emphasis on model uncertainty estimation and complexity, two of the central issues
  • Develop both time-domain and frequency-domain identification algorithms
  • Explain in detail uncertainty model validation concepts and techniques
  • Devote a chapter to a review of the requisite mathematics

Provide a concise yet self-contained appendix on several key relevant notions

About the Author

JIE CHEN, PhD, is Professor of Electrical Engineering at the University of California, Riverside. GUOXIANG GU, PhD, is Professor in the Department of Electrical and Computer Engineering at Louisiana State University, Baton Rouge.

Table of Contents

Mathematical Background.

H infinity Control and Identification.

Linear Algorithms.

Two-Stage Nonlinear Algorithms.

Nonlinear Interpolatory Algorithms.

Time Domain Algorithms.

H infinity Identification of Continuous-Time Systems.

Time Domain Model Validation.

Frequency Domain Model Validation.

Appendices.

References.

Index.

Product Details

ISBN:
9780471320487
Subtitle:
infin; Approach
Author:
Chen, Jie
Author:
Gu, Guoxiang
Publisher:
Wiley-Interscience
Location:
New York
Subject:
Engineering - General
Subject:
System Theory
Subject:
Robust control
Subject:
H (infinity symbol) control
Subject:
System identification
Subject:
Engineering-General Engineering
Subject:
Control Systems; Technology
Copyright:
Edition Description:
Includes bibliographical references and index.
Series:
Adaptive and Learning Systems for Signal Processing, Communications and Control Series
Series Volume:
19
Publication Date:
June 2000
Binding:
Electronic book text in proprietary or open standard format
Grade Level:
Professional and scholarly
Language:
English
Illustrations:
Yes
Pages:
443
Dimensions:
243 x 163.5 x 27 mm 26.8 oz

Related Subjects

Engineering » Engineering » General Engineering
Reference » Science Reference » Technology
Science and Mathematics » Electricity » General Electronics
Science and Mathematics » Mathematics » Systems Theory

Control Oriented System Identification
0 stars - 0 reviews
$ In Stock
Product details 443 pages John Wiley & Sons, Incorporated - English 9780471320487 Reviews:
"Synopsis" by , Provide a concise yet self-contained appendix on several key relevant notions
"Synopsis" by , A comprehensive, one-stop reference for new system modeling and identification tools

The field of control-oriented identification has grown immensely over the past decade, spawning numerous results and modeling techniques and promising the potential to influence science and engineering for years to come. In this new work, Jie Chen and Guoxiang Gu, two leading authorities on worst-case identification, share their vision and walk readers through carefully selected topics from the vast literature, offering a much-needed, timely comprehensive introduction to the theory of H? identification and model validation.

Chen and Gu clearly demonstrate the pros and cons of the worst-case approach in comparison to traditional techniques and provide researchers in systems and control theory with ready access to many new and complementary identification tools. Through a rigorous yet logical and easy-to-follow treatment, supported by many deep insights, intuitions, and philosophical thinking, they:

  • Survey and assess the current state of control and system identification research
  • Develop both two-stage and interpolatory algorithms for system identification
  • Show readers how to analyze the properties of linear algorithms
  • Offer a unique emphasis on model uncertainty estimation and complexity, two of the central issues
  • Develop both time-domain and frequency-domain identification algorithms
  • Explain in detail uncertainty model validation concepts and techniques
  • Devote a chapter to a review of the requisite mathematics

Provide a concise yet self-contained appendix on several key relevant notions

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