Sensitivity Analysis for Neural Networks
Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitiv
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Advisory Board: S. Amari G. Brassard K.A. De Jong C.C.A.M. Gielen T. Head L. Kari L. Landweber T. Martinetz Z. Michalewicz M.C. Mozer E. Oja G. P˘aun J. Reif H. Rubin A. Salomaa M. Schoenauer H.-P. Schwefel C. Torras D. Whitley E. Winfree J.M. Zurada
For further volumes: http://www.springer.com/series/4190
Daniel S. Yeung · Ian Cloete · Daming Shi · Wing W.Y. Ng
Sensitivity Analysis for Neural Networks
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Authors Prof. Daniel S. Yeung School of Computer Science and Engineering South China University of Technology Wushan Rd. TianHe District Guangzhou, China [email protected] Prof. Daming Shi School of Electrical Engineering and Computer Science Kyungpook National University Buk-gu, Daegu South Korea [email protected]
Prof. Ian Cloete President Campus 3 International University in Germany 76646 Bruchsal, Germany [email protected]; [email protected] Dr. Wing W.Y. Ng School of Computer Science and Engineering South China University of Technology Wushan Rd. TianHe District Guangzhou, China [email protected]
Series Editors G. Rozenberg (Managing Editor) [email protected] Th. Bäck, J.N. Kok, H.P. Spaink Leiden Center for Natural Computing Leiden University Niels Bohrweg 1 2333 CA Leiden, The Netherlands A.E. Eiben Vrije Universiteit Amsterdam The Netherlands
ISSN 1619-7127 ISBN 978-3-642-02531-0 e-ISBN 978-3-642-02532-7 DOI 10.1007/978-3-642-02532-7 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2009938187 ACM Computing Classification (1998): I.2.6, F.1.1 © Springer-Verlag Berlin Heidelberg 2010 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, 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. Cover design: KuenkelLopka GmbH Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
Neural networks provide a way to realize one of our human dreams to make machines think like us. Artificial neural networks have been developed since Rosenblatt proposed the Perceptron in 1958. Today, many neural networks are not treated as black boxes any more. Issues such as robustness and generalization abilities have been brought to the fore. The advances in neural networks have led to more and more practical applications in pattern recognition, financial engineering, automatic control and medical diagnosis, to
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