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Block-oriented Nonlinear Sy...
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: \2022 iterative and over-parameterization techniques; \2022 stochastic and frequency approaches; \2022 support-vector-machine, subspace, and separable-least-squares methods; \2022 blind identification method; \2022 bounded-error method; and \2022 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
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monografia Rebiun05099948 https://catalogo.rebiun.org/rebiun/record/Rebiun05099948 cr nn 008mamaa 100921s2010 xxk| s |||| 0|eng d 9781849965132 978-1-84996-513-2 10.1007/978-1-84996-513-2 doi UPNA0269293 UPVA 996893754103706 UPM 991005676028804212 UCAR 991007922520104213 UR0323006 UMO 110583 UPCT u359225 TJFM bicssc TEC004000 bisacsh 629.8 23 Giri, Fouad Block-oriented Nonlinear System Identification Recurso electrónico-En línea] edited by Fouad Giri, Er-Wei Bai London Springer London 2010 London London Springer London XIX, 431p. 50 illus. digital XIX, 431p. 50 illus. Lecture Notes in Control and Information Sciences 0170-8643 404 Engineering (Springer-11647) Block-oriented Nonlinear Systems -- Nonlinear System Modelling Based on Wiener Theory -- Parametric Identification of Hammerstein: Over-parameterization Approach -- Compound Operator Decomposition and Its Application to Hammerstein and Wiener Systems -- Iterative Identification of Parametric Hammerstein -- Hammerstein System Identification -- Maximum Likelihood Estimation of Wiener Models -- Stochastic Identification of Wiener Systems -- Convergence Rate in Nonparametric and Semi-parametric Identification -- Identification of Block-oriented Systems Using the Invariance Property -- Frequency Identification of Hammerstein Systems -- Frequency Identification of Wiener Systems -- Frequency Identification of Wiener and Hammerstein Systems -- Kernel-based Methods in the Identification of Wiener\2013Hammerstein Systems in Closed Loop within a Subspace Context -- Least-squares Support Vector Machine Estimation of Hammerstein Systems -- Separable Least-squares Estimation of Hammerstein with Hard Nonlinearities -- Blind Identification of Block Structures with Unknown Input -- Blind Identification of Hammerstein\2013Wiener Systems -- Nonlinear Filtered-algorithm for a Class of Parallel Hammerstein Systems -- Bounded Error Identification of Hammersteini with Backlash -- Decouple Input Hammerstein System Identification -- Identification of Block-oriented System with Input Backlash -- Block-oriented Models in Biomedical/Physiological Applications -- Block-oriented Modelling in Biological Systems Accesible sólo para usuarios de la UPV Recurso a texto completo 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: \2022 iterative and over-parameterization techniques; \2022 stochastic and frequency approaches; \2022 support-vector-machine, subspace, and separable-least-squares methods; \2022 blind identification method; \2022 bounded-error method; and \2022 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 Reproducción electrónica Forma de acceso: Web Engineering Biological models Biology-Mathematics Systems theory Engineering Control Systems Theory, Control Systems Biology Biological Networks, Systems Biology Mathematical Biology in General Automotive Engineering Bai, Er-Wei SpringerLink (Servicio en línea) Springer eBooks Springer eBooks Printed edition 9781849965125 Lecture Notes in Control and Information Sciences 0170-8643 404