Modelling and parameter estimation of dynamic systems /
General Material Designation
[Book]
First Statement of Responsibility
J.R. Raol, G. Girija and J. Singh.
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
London :
Name of Publisher, Distributor, etc.
Institution of Electrical Engineers,
Date of Publication, Distribution, etc.
2004.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (xiv, 388 pages) :
Other Physical Details
illustrations
SERIES
Series Title
IEE control engineering series ;
Volume Designation
65
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
Text of Note
Preface; Acknowledgements; 1 Introduction; 2 Least squares methods; 3 Output error method; 4 Filtering methods; 5 Filter error method; 6 Determination of model order and structure; 7 Estimation before modelling approach; 8 Approach based on the concept of model error; 9 Parameter estimation approaches for unstable/augmented systems; 10 Parameter estimation using artificial neural networks and genetic algorithms; 11 Real-time parameter estimation; Bibliography; Appendix A: Properties of signals, matrices, estimators and estimates; Appendix B: Aircraft models for parameter estimation
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SUMMARY OR ABSTRACT
Text of Note
Parameter estimation is the process of using observations from a system to develop mathematical models that adequately represent the system dynamics. The assumed model consists of a finite set of parameters, the values of which are calculated using estimation techniques. Most of the techniques that exist are based on least-square minimization of error between the model response and actual system response. However, with the proliferation of high speed digital computers, elegant and innovative techniques like filter error method, H-infinity and Artificial Neural Networks are finding more and mor.
OTHER EDITION IN ANOTHER MEDIUM
Title
Modelling and parameter estimation of dynamic systems.