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  • 1
    ISBN: 9783030958602
    Language: Undetermined
    Pages: 1 Online-Ressource (377 p.)
    Series Statement: Communications and Control Engineering
    Keywords: Machine learning ; Automatic control engineering ; Statistical physics ; Bayesian inference ; Probability & statistics ; Cybernetics & systems theory
    Abstract: This 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
    Note: English
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  • 2
    Online Resource
    Online Resource
    Upper Saddle River, N.J. : Prentice Hall
    ISBN: 9780132441933 , 0132441934
    Language: English
    Pages: 1 online resource (xxii, 609 p.) , ill.
    Edition: 2nd ed.
    Series Statement: Prentice Hall information and system sciences series
    Parallel Title: Erscheint auch als
    Keywords: System identification ; Electronic books ; local
    Abstract: The field's leading text, now completely updated. Modeling dynamical systems - theory, methodology, and applications. Lennart Ljung's System Identification: Theory for the User is a complete, coherent description of the theory, methodology, and practice of System Identification. This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and general non-linear black box methods, including neural networks and neuro-fuzzy modeling. The book contains many new computer-based examples designed for Ljung's market-leading software, System Identification Toolbox for MATLAB. Ljung combines careful mathematics, a practical understanding of real-world applications, and extensive exercises. He introduces both black-box and tailor-made models of linear as well as non-linear systems, and he describes principles, properties, and algorithms for a variety of identification techniques: Nonparametric time-domain and frequency-domain methods. Parameter estimation methods in a general prediction error setting. Frequency domain data and frequency domain interpretations. Asymptotic analysis of parameter estimates. Linear regressions, iterative search methods, and other ways to compute estimates. Recursive (adaptive) estimation techniques. Ljung also presents detailed coverage of the key issues that can make or break system identification projects, such as defining objectives, designing experiments, controlling the bias distribution of transfer-function estimates, and carefully validating the resulting models. The first edition of System Identification has been the field's most widely cited reference for over a decade. This new edition will be the new text of choice for anyone concerned with system identification theory and practice.
    Note: Description based on print version record. - Includes bibliographical references (p. 565-593) and indexes
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