Subspace Identification for Linear Systems Theory — Implementation — Applications
The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are wellestablished algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of clas...
Main Authors:  , 

Format:  eBook 
Language:  English 
Published: 
New York, NY
Springer US
1996, 1996

Edition:  1st ed. 1996 
Subjects:  
Online Access:  
Collection:  Springer Book Archives 2004  Collection details see MPG.ReNa 
Table of Contents:
 1 Introduction, Motivation and Geometric Tools
 1.1 Models of systems and system identification
 1.2 A new generation of system identification algorithms
 1.3 Overview
 1.4 Geometric tools
 1.5 Conclusions
 2 Deterministic Identification
 2.1 Deterministic systems
 2.2 Geometric properties of deterministic systems
 2.3 Relation to other algorithms
 2.4 Computing the system matrices
 2.5 Conclusions
 3 Stochastic Identification
 3.1 Stochastic systems
 3.2 Geometric properties of stochastic systems
 3.3 Relation to other algorithms
 3.4 Computing the system matrices
 3.5 Conclusions
 4 Combined DeterministicStochastic Identification
 4.1 Combined systems
 4.2 Geometric properties of combined systems
 4.3 Relation to other algorithms
 4.4 Computing the system matrices
 4.5 Connections to the previous Chapters
 4.6 Conclusions
 5 State Space Bases and Model Reduction
 5.1 Introduction
 5.2 Notation
 5.3 Frequency weighted balancing
 5.4 Subspace identification and frequency weighted balancing
 5.5 Consequences for reduced order identification
 5.6 Example
 5.7 Conclusions
 6 Implementation and Applications
 6.1 Numerical Implementation
 6.2 Interactive System Identification
 6.3 An Application of ISID
 6.4 Practical examples in Matlab
 6.5 Conclusions
 7 Conclusions and Open Problems
 7.1 Conclusions
 7.2 Open problems
 A Proofs
 A.1 Proof of formula (2.16)
 A.2 Proof of Theorem 6
 A.3 Note on the special form of the Kalman filter
 A.4 Proof of Theorem 8
 A.5 Proof of Theorem 9
 A.6 Proof of Theorem 11
 A.7 Proof of Theorem 12
 A.8 Proof of Lemma 2
 A.9 Proof of Theorem 13
 A.10 Proof of Corollary 2 and 3
 A.11 Proof of Theorem 14
 B Matlab Functions
 B.1 Getting started
 B.2 Matlab Reference
 B.2.1 Directory: ‘subfun’
 B.2.2 Directory: ‘applic’
 B.2.3 Directory: ‘examples’
 B.2.4 Directory: ‘figures’
 C Notation
 References