Block-oriented Nonlinear System Identification: (404 Lecture Notes in Control and Information Sciences)

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.

Table of Contents:
Block-oriented Nonlinear Models.- to Block-oriented Nonlinear Systems.- Nonlinear System Modelling and Analysis from the Volterra and Wiener Perspective.- Iterative and Overparameterization Methods.- An Optimal Two-stage Identification Algorithm for Hammerstein–Wiener Nonlinear Systems.- Compound Operator Decomposition and Its Application to Hammerstein and Wiener Systems.- Iterative Identification of Hammerstein Systems.- Stochastic Methods.- Recursive Identification for Stochastic Hammerstein Systems.- Wiener System Identification Using the Maximum Likelihood Method.- Parametric Versus Nonparametric Approach to Wiener Systems Identification.- Identification of Block-oriented Systems: Nonparametric and Semiparametric Inference.- Identification of Block-oriented Systems Using the Invariance Property.- Frequency Methods.- Frequency Domain Identification of Hammerstein Models.- Frequency Identification of Nonparametric Wiener Systems.- Identification of Wiener–Hammerstein Systems Using the Best Linear Approximation.- SVM, Subspace and Separable Least-squares.- Subspace Identification of Hammerstein–Wiener Systems Operating in Closed-loop.- NARX Identification of Hammerstein Systems Using Least-Squares Support Vector Machines.- Identification of Linear Systems with Hard Input Nonlinearities of Known Structure.- Blind Methods.- Blind Maximum-likelihood Identification of Wiener and Hammerstein Nonlinear Block Structures.- A Blind Approach to Identification of Hammerstein Systems.- A Blind Approach to the Hammerstein-Wiener Model Identification.- Decoupling Inputs and Bounded Error Methods.- Decoupling the Linear and Nonlinear Parts in Hammerstein Model Identification.- Hammerstein System Identification in Presence of Hard Memory Nonlinearities.- Bounded ErrorIdentification of Hammerstein Systems with Backlash.- Application of Block-oriented Models.- Block Structured Modelling in the Study of the Stretch Reflex.- Application of Block-oriented System Identification to Modelling Paralysed Muscle Under Electrical Stimulation.

About the Author :
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

Review :
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)