001431390 000__ 03842cam\a2200517Ii\4500
001431390 001__ 1431390
001431390 003__ OCoLC
001431390 005__ 20230308003234.0
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001431390 007__ cr\un\nnnunnun
001431390 008__ 220519s2022\\\\sz\a\\\\ob\\\\001\0\eng\d
001431390 020__ $$a9783030958602$$q(electronic bk.)
001431390 020__ $$a3030958604$$q(electronic bk.)
001431390 020__ $$z9783030958596$$q(print)
001431390 0247_ $$a10.1007/978-3-030-95860-2$$2doi
001431390 035__ $$aSP(OCoLC)1319038749
001431390 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dOCLCF$$dOCLCQ
001431390 049__ $$aISEA
001431390 050_4 $$aQA402
001431390 08204 $$a003/.1$$223/eng/20220519
001431390 1001_ $$aPillonetto, Gianluigi,$$eauthor.
001431390 24510 $$aRegularized system identification :$$blearning dynamic models from data /$$cGianluigi Pillonetto, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, Lennart Ljung.
001431390 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001431390 300__ $$a1 online resource (xxiv, 377 pages) :$$billustrations (some color).
001431390 336__ $$atext$$btxt$$2rdacontent
001431390 337__ $$acomputer$$bc$$2rdamedia
001431390 338__ $$aonline resource$$bcr$$2rdacarrier
001431390 4901_ $$aCommunications and control engineering,$$x2197-7119
001431390 504__ $$aIncludes bibliographical references and index.
001431390 5050_ $$aChapter 1. Bias -- Chapter 2. Classical System Identification -- Chapter 3. Regularization of Linear Regression Models -- Chapter 4. Bayesian Interpretation of Regularization -- Chapter 5. Regularization for Linear System Identification -- Chapter 6. Regularization in Reproducing Kernel Hilbert Spaces -- Chapter 7. Regularization in Reproducing Kernel Hilbert Spaces for Linear System Identification -- Chapter 8. Regularization for Nonlinear System Identification -- Chapter 9. Numerical Experiments and Real-World Cases.
001431390 5060_ $$aOpen access.$$5GW5XE
001431390 520__ $$aThis open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. In many ways, this book is a complement and continuation of the much-used text book L. Ljung, System Identification, 978-0-13-656695-3. This is an open access book.
001431390 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed May 19, 2022).
001431390 650_0 $$aSystem identification.
001431390 655_0 $$aElectronic books.
001431390 7001_ $$aChen, Tianshi,$$eauthor.
001431390 7001_ $$aChiuso, Alessandro,$$eauthor.
001431390 7001_ $$aDe Nicolao, Giuseppe,$$eauthor.
001431390 7001_ $$aLjung, Lennart,$$eauthor.
001431390 830_0 $$aCommunications and control engineering,$$x2197-7119
001431390 852__ $$bebk
001431390 85640 $$3Springer Nature$$uhttps://link.springer.com/10.1007/978-3-030-95860-2$$zOnline Access$$91397441.2
001431390 909CO $$ooai:library.usi.edu:1431390$$pGLOBAL_SET
001431390 980__ $$aBIB
001431390 980__ $$aEBOOK
001431390 982__ $$aEbook
001431390 983__ $$aOnline
001431390 994__ $$a92$$bISE