Adaptive learning methods for nonlinear system modeling / / edited by Danilo Comminiello, José C. Principe

• Autore: Comminiello Danilo
• Pubblicazione: Kidlington, Oxford, United Kingdom : , : Butterworth-Heinemann, an imprint of Elsevier, , [2018]; ©2018
• Edizione: First edition.
• Descrizione fisica: 1 online resource (390 pages)
• Disciplina: 621.3822
• Soggetto topico: Adaptive signal processing
• Persona (resp. second.): ComminielloDanilo; PríncipeJ. C (José C.)
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Note continued: 8.2.4.Semiparametric Reconstruction -- 8.2.5.Numerical Tests -- 8.3.Inference of Dynamic Functions Over Dynamic Graphs -- 8.3.1.Kernels on Extended Graphs -- 8.3.2.Multikernel Kriged Kalman Filters -- 8.3.3.Numerical Tests -- 8.3.4.Summary -- Acknowledgments -- References -- pt. 3 NONLINEAR MODELING WITH MULTIPLE LEARNING MACHINES -- ch. 9 Online Nonlinear Modeling via Self-Organizing Trees -- 9.1.Introduction -- 9.2.Self-Organizing Trees for Regression Problems -- 9.2.1.Notation -- 9.2.2.Construction of the Algorithm -- 9.2.3.Convergence of the Algorithm -- 9.3.Self-Organizing Trees for Binary Classification Problems -- 9.3.1.Construction of the Algorithm -- 9.3.2.Convergence of the Algorithm -- 9.4.Numerical Results -- 9.4.1.Numerical Results for Regression Problems -- 9.4.2.Numerical Results for Classification Problems -- Appendix 9.A -- 9.A.1.Proof of Theorem 1 -- 9.A.2.Proof of Theorem 2 -- Acknowledgments -- References
• Sommario/riassunto: Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.
• Titolo autorizzato: Adaptive learning methods for nonlinear system modeling
• ISBN: 0-12-812977-8; 0-12-812976-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910583390803321
• Lo trovi qui: Univ. Federico II