Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
The present book is devoted to problems of adaptation ofartificial neural networks to robust fault diagnosis schemes. Itpresents neural networks-based modelling and estimation techniques usedfor designing robust fault diagnosis schemes for non-linear dyna
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Marcin Mrugalski
Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
Studies in Computational Intelligence Volume 510
Series Editor Janusz Kacprzyk, Warsaw, Poland
For further volumes: http://www.springer.com/series/7092
Marcin Mrugalski
Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis
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Marcin Mrugalski Institute of Control and Computation Engineering Faculty of Electrical Engineering, Computer Science and Telecommunications University of Zielona Góra Zielona Góra Poland
ISSN 1860-949X ISBN 978-3-319-01546-0 DOI 10.1007/978-3-319-01547-7
ISSN 1860-9503 (electronic) ISBN 978-3-319-01547-7 (eBook)
Springer Cham Heidelberg New York Dordrecht London Library of Congress Control Number: 2013943920 c Springer International Publishing Switzerland 201 4 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.
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To my beloved and wonderful wife Beata
Foreword
The artificial neural networks belonging to dynamically developed soft computing methods are widely applied in several modern scientific fields. These advanced computational methods due to their unique properties are used to explore the fields of pattern recognition, signal processing, financial forecasting, modelling and identification, process monitoring
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