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OverviewThis book offers a tutorial view of recent trends in the science of modelling, adaptation, and learning. The most important modern approaches to identification, namely the stochastic, behavioral, subspace, and frequency domain approaches, are discussed thoroughly. On adaptation, tuning the parameters of a linear model is presented as a cure for uncertainty, and the asymptotics of recursive least squares and self-tuning systems are explained simply after a fully deterministic analysis. For constructing nonlinear models from data, neural networks and wavelets are considered as useful nonlinear tools, and fuzzy logic is presented as a way of coping with qualitative information. A final chapter deals with optimization methods. The book will become an important reference for researchers in the field. Full Product DetailsAuthor: Sergio Bittanti , Giorgio PicciPublisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K Edition: Softcover reprint of hardcover 1st ed. 1996 Volume: 153 Dimensions: Width: 15.50cm , Height: 3.30cm , Length: 23.50cm Weight: 0.884kg ISBN: 9783642082481ISBN 10: 3642082483 Pages: 552 Publication Date: 09 December 2010 Audience: Professional and scholarly , Professional and scholarly , Professional & Vocational , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsGeometric Methods for State Space Identification.- Parameter Estimation of Multivariable Systems Using Balanced Realizations.- Balanced Canonical Forms.- From Data to State Model.- Identification of Linear Systems from Noisy Data.- Identification in H?: Theory and Applications.- System Identification with Information Theoretic Criteria.- Least Squares Based Self-Tuning Control Systems.- On Neural Network Model Structures in System Identification.- An Overview of Computational Learning Theory and Its Applications to Neural Network Training.- Just-in-Time Learning and Estimation.- Wavelets in Identification.- Fuzzy Logic Modelling and Control.- Searching for the Best: Stochastic Approximation, Simulated Annealing and Related Procedures.- List of Contributors.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |