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Identification, Adaptation, Learning: The Science of Learning Models from Data (Data and Knowledge in a Changing World) (Hardback)
$243.28 - Free delivery worldwide (to United States and
all these other countries) Usually dispatched within 7 days | |Short Description for Identification, Adaptation, LearningThis book offers a tutorial view of the science of learning models from data. It covers the most important approaches to linear modelling, nonlinear model construction from data, fuzzy logic based modelling, and optimization methods.
Full description- Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
- Published: 16 August 1996
- Format: Hardback 574 pages
- See: Full bibliographic data
- Categories: Number Systems | Probability & Statistics | Automatic Control Engineering | 3D Graphics & Modelling | Computer Science | Artificial Intelligence | Machine Learning | Computer Vision
- ISBN 13: 9783540610809 ISBN 10: 3540610804
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Full description for Identification, Adaptation, Learning
This 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.
