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From Data to Model (Paperback, Softcover reprint of the original 1st ed. 1989)
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From Data to Model (Paperback, Softcover reprint of the original 1st ed. 1989)
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The problem of obtaining dynamical models directly from an observed
time-series occurs in many fields of application. There are a
number of possible approaches to this problem. In this volume a
number of such points of view are exposed: the statistical time
series approach, a theory of guaranted performance, and finally a
deterministic approximation approach. This volume is an out-growth
of a number of get-togethers sponsered by the Systems and Decision
Sciences group of the International Institute of Applied Systems
Analysis (IIASA) in Laxenburg, Austria. The hospitality and support
of this organization is gratefully acknowledged. Jan Willems
Groningen, the Netherlands May 1989 TABLE OF CONTENTS Linear System
Identification- A Survey page 1 M. Deistler A Tutorial on
Hankel-Norm Approximation 26 K. Glover A Deterministic Approach to
Approximate Modelling 49 C. Heij and J. C. Willems Identification -
a Theory of Guaranteed Estimates 135 A. B. Kurzhanski Statistical
Aspects of Model Selection 215 R. Shibata Index 241 Addresses of
Authors 246 LINEAR SYSTEM IDENTIFICATION* A SURVEY M. DEISTLER
Abstract In this paper we give an introductory survey on the theory
of identification of (in general MIMO) linear systems from
(discrete) time series data. The main parts are: Structure theory
for linear systems, asymptotic properties of maximum likelihood
type estimators, estimation of the dynamic specification by methods
based on information criteria and finally, extensions and
alternative approaches such as identification of unstable systems
and errors-in-variables. Keywords Linear systems, parametrization,
maximum likelihood estimation, information criteria,
errors-in-variables.
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