Locally Recurrent Neural Networks
Artificial neural networks provide an excellent mathematical tool for dealing with non-linear problems [18, 23, 77]. They have an important property according to which any continuous non-linear relation can be approximated with arbitrary accuracy using a
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Krzysztof Patan
Artificial Neural Networks for the Modelling and Fault Diagnosis of Technical Processes
ABC
Series Advisory Board F. Allgöwer, P. Fleming, P. Kokotovic, A.B. Kurzhanski, H. Kwakernaak, A. Rantzer, J.N. Tsitsiklis
Author Krzysztof Patan University of Zielona Góra Inst. Control and Computation Engineering ul. Podgórna 50 65-246 Zielona Góra Poland E-Mail: [email protected]
ISBN 978-3-540-79871-2
e-ISBN 978-3-540-79872-9
DOI 10.1007/978-3-540-79872-9 Lecture Notes in Control and Information Sciences
ISSN 0170-8643
Library of Congress Control Number: 2008926085 c 2008
Springer-Verlag Berlin Heidelberg
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To my beloved wife Agnieszka, and children Weronika and Leonard, for their patience and tolerance
Foreword
An unappealing characteristic of all real-world systems is the fact that they are vulnerable to faults, malfunctions and, more generally, unexpected modes of behaviour. This explains why there is a continuous need for reliable and universal monitoring systems based on suitable and effective fault diagnosis strategies. This is especially true for engineering systems, whose complexity is permanently growing due to the inevitable development of modern industry as well as the information and communication technology revolution. Indeed, the design and operation of engineering systems require an increased attention with respect to availability, reliability, safety and fault tolerance. Thus, it is natural that fault diagnosis plays a fundamental role in modern control theory and practice. This is reflected in plenty of papers on fault diagnosis in many control-oriented conferences and journals. Indeed, a large amount of knowledge on model based fault diagnosis has been accumulated through scientific literature since the beginning of the 1970s. As a result, a wide spectrum of fault diagnosis techniques have been developed. A major category of fault diagnosis techniques is the model based one, where an analytical model of the plant to be monitored is assumed to be available. Unfortunately, a fundamental difficulty relate
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