Dynamic Systems Models New Methods of Parameter and State Estimation
This monograph is an exposition of a novel method for solving inverse problems, a method of parameter estimation for time series data collected from simulations of real experiments. These time series might be generated by measuring the dynamics of aircraf
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Dynamic Systems Models New Methods of Parameter and State Estimation
Dynamic Systems Models
Josif A. Boguslavskiy
Dynamic Systems Models New Methods of Parameter and State Estimation Mark Borodovsky Editor
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Josif A. Boguslavskiy (deceased) State Scientific Research Institute of Automated Systems Moscow Russia
MATLAB is a registered trademark of The MathWorks, Inc., 3 Apple Hill Drive, Natick, MA 01760-2098, USA, http://www.mathworks.com. ISBN 978-3-319-04035-6 ISBN 978-3-319-04036-3 DOI 10.1007/978-3-319-04036-3 Springer Cham Heidelberg New York Dordrecht London
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Library of Congress Control Number: 2014930189 Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Contents
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Linear Estimators of a Random Parameter Vector . . . . . . . . . 1.1 Linear Estimator, Optimal in the Root-Mean-Square Sense . 1.2 Vector Measure of Nonlinearity of Vector Y1 in Relation to Vector h . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Decomposition of Path of Observations to the Recurrence Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Recurrent Form of Algorithm for Estimator Vector . . . . . . 1.5 Problem of Optimal Linear Filtration . . . . . .
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