LEADER 04483nam 22006135 450 001 9910483898303321 005 20200629154314.0 010 $a3-030-00193-8 024 7 $a10.1007/978-3-030-00193-3 035 $a(CKB)4100000006999376 035 $a(DE-He213)978-3-030-00193-3 035 $a(MiAaPQ)EBC5921635 035 $z(PPN)258847271 035 $a(PPN)243764235 035 $a(EXLCZ)994100000006999376 100 $a20181107d2019 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aHybrid System Identification $eTheory and Algorithms for Learning Switching Models /$fby Fabien Lauer, Gérard Bloch 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XXI, 253 p. 35 illus., 34 illus. in color.) 225 1 $aLecture Notes in Control and Information Sciences,$x0170-8643 ;$v478 300 $aIncludes index. 311 $a3-030-00192-X 327 $aIntroduction -- System Identification -- Classification -- Hybrid System Identification -- Exact Methods for Hybrid System Identification -- Estimation of Switched Linear/Affine Models -- Estimation of Piecewise Affine Models -- Recursive and State-space Identification of Hybrid Systems -- Nonlinear Hybrid System Identification. 330 $aHybrid System Identification helps readers to build mathematical models of dynamical systems switching between different operating modes, from their experimental observations. It provides an overview of the interaction between system identification, machine learning and pattern recognition fields in explaining and analysing hybrid system identification. It emphasises the optimization and computational complexity issues that lie at the core of the problems considered and sets them aside from standard system identification problems. The book presents practical methods that leverage this complexity, as well as a broad view of state-of-the-art machine learning methods. The authors illustrate the key technical points using examples and figures to help the reader understand the material. The book includes an in-depth discussion and computational analysis of hybrid system identification problems, moving from the basic questions of the definition of hybrid systems and system identification to methods of hybrid system identification and the estimation of switched linear/affine and piecewise affine models. The authors also give an overview of the various applications of hybrid systems, discuss the connections to other fields, and describe more advanced material on recursive, state-space and nonlinear hybrid system identification. Hybrid System Identification includes a detailed exposition of major methods, which allows researchers and practitioners to acquaint themselves rapidly with state-of-the-art tools. The book is also a sound basis for graduate and undergraduate students studying this area of control, as the presentation and form of the book provides the background and coverage necessary for a full understanding of hybrid system identification, whether the reader is initially familiar with system identification related to hybrid systems or not. 410 0$aLecture Notes in Control and Information Sciences,$x0170-8643 ;$v478 606 $aControl engineering 606 $aSystem theory 606 $aArchitecture, Computer 606 $aControl and Systems Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/T19010 606 $aSystems Theory, Control$3https://scigraph.springernature.com/ontologies/product-market-codes/M13070 606 $aComputer System Implementation$3https://scigraph.springernature.com/ontologies/product-market-codes/I13057 615 0$aControl engineering. 615 0$aSystem theory. 615 0$aArchitecture, Computer. 615 14$aControl and Systems Theory. 615 24$aSystems Theory, Control. 615 24$aComputer System Implementation. 676 $a004.259 676 $a629.89 700 $aLauer$b Fabien$4aut$4http://id.loc.gov/vocabulary/relators/aut$01228427 702 $aBloch$b Gérard$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483898303321 996 $aHybrid System Identification$92851845 997 $aUNINA