Block-oriented Nonlinear System Identification

1. Front Matter

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2. Block-oriented Nonlinear Models

   1. Front Matter

      Pages 1-1

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   2. Introduction to Block-oriented Nonlinear Systems

      • Er-Wei Bai, Fouad Giri

      Pages 3-11

   3. Nonlinear System Modelling and Analysis from the Volterra and Wiener Perspective

      • Martin Schetzen

      Pages 13-24

3. Iterative and Overparameterization Methods

   1. Front Matter

      Pages 25-25

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   2. An Optimal Two-stage Identification Algorithm for Hammerstein–Wiener Nonlinear Systems

      • Er-Wei Bai

      Pages 27-34

   3. Compound Operator Decomposition and Its Application to Hammerstein and Wiener Systems

      • Jozef Vörös

      Pages 35-51

   4. Iterative Identification of Hammerstein Systems

      • Yun Liu, Er-Wei Bai

      Pages 53-65

4. Stochastic Methods

   1. Front Matter

      Pages 67-67

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   2. Recursive Identification for Stochastic Hammerstein Systems

      • Han-Fu Chen

      Pages 69-87

   3. Wiener System Identification Using the Maximum Likelihood Method

      • Adrian Wills, Lennart Ljung

      Pages 89-110

   4. Parametric Versus Nonparametric Approach to Wiener Systems Identification

      • Grzegorz Mzyk

      Pages 111-125

   5. Identification of Block-oriented Systems: Nonparametric and Semiparametric Inference

      • M. Pawlak

      Pages 127-146

   6. Identification of Block-oriented Systems Using the Invariance Property

      • Martin Enqvist

      Pages 147-158

5. Frequency Methods

   1. Front Matter

      Pages 159-159

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   2. Frequency Domain Identification of Hammerstein Models

      • Er-Wei Bai

      Pages 161-180

   3. Frequency Identification of Nonparametric Wiener Systems

      • Fouad Giri, Youssef Rochdi, Jean-Baptiste Gning, Fatima-Zahra Chaoui

      Pages 181-207

   4. Identification of Wiener–Hammerstein Systems Using the Best Linear Approximation

      • Lieve Lauwers, Johan Schoukens

      Pages 209-225

6. SVM, Subspace and Separable Least-squares

   1. Front Matter

      Pages 227-227

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   2. Subspace Identification of Hammerstein–Wiener Systems Operating in Closed-loop

      • Jan-Willem van Wingerden, Michel Verhaegen

      Pages 229-239

Block-oriented Nonlinear System Identification deals with an area of research that has been very active since the turn of the millennium. The book makes a pedagogical and cohesive presentation of the methods developed in that time. These include: iterative and over-parameterization techniques; stochastic and frequency approaches; support-vector-machine, subspace, and separable-least-squares methods; blind identification method; bounded-error method; and decoupling inputs approach. The identification methods are presented by authors who have either invented them or contributed significantly to their development. All the important issues e.g., input design, persistent excitation, and consistency analysis, are discussed. The practical relevance of block-oriented models is illustrated through biomedical/physiological system modelling. The book will be of major interest to all those who are concerned with nonlinear system identification whatever their activity areas. This is particularly the case for educators in electrical, mechanical, chemical and biomedical engineering and for practising engineers in process, aeronautic, aerospace, robotics and vehicles control. Block-oriented Nonlinear System Identification serves as a reference for active researchers, new comers, industrial and education practitioners and graduate students alike.

• Block-orientated Systems
• Control
• Cross000
• Hammerstein
• Nonlinear Systems
• Nonlinear system
• Parameter Estimation
• Systems Identification
• Wiener
• system
• system identification
• Systems Biology

From the reviews:

“The book covers a wide range of topics dealing with identification of block-oriented nonlinear structures. The topics are carefully selected and no important topics are left out. Many topics are treated in depth using a plethora of tools from probability theory, statistics and signal theory. This makes the book an indispensable and valuable resource for any researcher in the field of nonlinear system identification.” (Adam Krzyżak, Mathematical Reviews, Issue 2012 e)

• Laboratoire GREYC, Ensicaen, Caen Cedex, France

  Fouad Giri

• Seamans Center for the Engineering Arts and Sciences, The University of Iowa, Iowa City, USA

  Er-Wei Bai

Fouad GIRI received his PhD in automatic control from the Institut National Polytechnique of Grenoble, France, in 1988. He is Professor at the University of Caen basse-Normandie, Caen, France and member of the GREYC Lab (CNRS UMR 6072). He has served as associate editor for several journals including Control Engineering Practice and as a member of IFAC Technical Commitees on Modeling Identification and Signal Processing, and Adaptive and Learning Systems. His research interests include nonlinear system identification, nonlinear, adaptive and constrained control, and the application of identification and control theory to power converters and electric machines. He has published over 170 journal/conference papers on these topics and co-authored two textbooks on automatic control.

Er-Wei BAI received his PhD from the University of California in Berkeley in 1987 and is Professor of Electrical Engineering at the University of Iowa. He is an IEEE Fellow and an author/co-author of over 140 journal papers in the area. He has served as associate editor for a number of journals including IEEE Transactions on Automatic Control and Automatica. He currently serves as a member of the IEEE CSS Technical Committee on Identification and Adaptive Control and the IFAC Technical Committee on Modeling, Identification and Signal Processing

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