Block-oriented Nonlinear System Identification [electronic resource] /
Giri, Fouad.editor.
Bai, Er-Wei.editor.
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 modeling. 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, newcomers, industrial and education practitioners and graduate students alike.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
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 Error Identification 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.
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 modeling. 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, newcomers, industrial and education practitioners and graduate students alike.
Control engineering.
Life sciences.
Applied mathematics.
Engineering mathematics.
System theory.
Systems biology.
Biological systems.
Control and Systems Theory.
Life Sciences, general.
Applications of Mathematics.
Systems Theory, Control.
Systems Biology.
Systems Biology.
Lecture notes in control and information sciences