LEADER 03712nam 22007333u 450 001 9910568256103321 005 20231110212430.0 010 $a3-030-95860-4 035 $a(CKB)5680000000039096 035 $aEBL6986548 035 $a(OCoLC)1319038749 035 $a(AU-PeEL)EBL6986548 035 $a(MiAaPQ)EBC6986548 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/84390 035 $a(EXLCZ)995680000000039096 100 $a20220617d2022|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRegularized System Identification $eLearning Dynamic Models from Data 210 $aCham $cSpringer International Publishing AG$d2022 215 $a1 online resource (394 p.) 225 1 $aCommunications and Control Engineering 300 $aDescription based upon print version of record. 311 $a3-030-95859-0 330 $aThis open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors? reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book. 410 0$aCommunications and Control Engineering 606 $aMachine learning$2bicssc 606 $aAutomatic control engineering$2bicssc 606 $aStatistical physics$2bicssc 606 $aBayesian inference$2bicssc 606 $aProbability & statistics$2bicssc 606 $aCybernetics & systems theory$2bicssc 610 $aSystem Identification 610 $aMachine Learning 610 $aLinear Dynamical Systems 610 $aNonlinear Dynamical Systems 610 $aKernel-based Regularization 610 $aBayesian Interpretation of Regularization 610 $aGaussian Processes 610 $aReproducing Kernel Hilbert Spaces 610 $aEstimation Theory 610 $aSupport Vector Machines 610 $aRegularization Networks 615 7$aMachine learning 615 7$aAutomatic control engineering 615 7$aStatistical physics 615 7$aBayesian inference 615 7$aProbability & statistics 615 7$aCybernetics & systems theory 700 $aPillonetto$b Gianluigi$01231715 701 $aChen$b Tianshi$01236794 701 $aChiuso$b Alessandro$01236795 701 $aDe Nicolao$b Giuseppe$0799498 701 $aLjung$b Lennart$028309 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910568256103321 996 $aRegularized System Identification$92871538 997 $aUNINA