
Identification, Adaptation, Learning
The Science of Learning Models from Data
Springer (Publisher)
Published on 9. December 2010
Book
Paperback/Softback
XXI, 552 pages
978-3-642-08248-1 (ISBN)
Description
This book collects the lectures given at the NATO Advanced Study Institute From Identijication to Learning held in Villa Olmo, Como, Italy, from August 22 to September 2, 1994. The school was devoted to the themes of Identijication, Adaptation and Learning, as they are currently understood in the Information and Contral engineering community, their development in the last few decades, their inter connections and their applications. These titles describe challenging, exciting and rapidly growing research areas which are of interest both to contral and communication engineers and to statisticians and computer scientists. In accordance with the general goals of the Institute, and notwithstanding the rat her advanced level of the topics discussed, the presentations have been generally kept at a fairly tutorial level. For this reason this book should be valuable to a variety of rearchers and to graduate students interested in the general area of Control, Signals and Information Pracessing. As the goal of the school was to explore a common methodologicalline of reading the issues, the flavor is quite interdisciplinary. We regard this as an original and valuable feature of this book.
More details
Series
Edition
1st ed. Softcover of orig. ed. 1996
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XXI, 552 p.
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 32 mm
Weight
834 gr
ISBN-13
978-3-642-08248-1 (9783642082481)
DOI
10.1007/978-3-662-03295-4
Schweitzer Classification
Other editions
Additional editions

Sergio Bittanti | Giorgio Picci
Identification, Adaptation, Learning
The Science of Learning Models from Data
Book
07/1996
Springer
€213.99
Shipment within 10-15 days
Content
Geometric 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.