Applications of Neural Networks in High Assurance Systems

  • Johann Schumann
  • Yan Liu

Part of the Studies in Computational Intelligence book series (SCI, volume 268)

Table of contents

  1. Front Matter
  2. Johann Schumann, Pramod Gupta, Yan Liu
    Pages 1-19
  3. Anuradha M. Annaswamy, Jinho Jang, Eugene Lavretsky
    Pages 21-39
  4. Tim Smith, Jim Barhorst, James M. Urnes Sr.
    Pages 57-76
  5. Sampath Yerramalla, Edgar Fuller, Bojan Cukic
    Pages 111-139
  6. Dragan Djurdjanovic, Jianbo Liu, Kenneth A. Marko, Jun Ni
    Pages 141-163
  7. Y. D. Song, Liguo Weng, Medorian D. Gheorghiu
    Pages 165-178

About this book


"Applications of Neural Networks in High Assurance Systems" is the first book directly addressing a key part of neural network technology: methods used to pass the tough verification and validation (V&V) standards required in many safety-critical applications. The book presents what kinds of evaluation methods have been developed across many sectors, and how to pass the tests. A new adaptive structure of V&V is developed in this book, different from the simple six sigma methods usually used for large-scale systems and different from the theorem-based approach used for simplified component subsystems.


Aerospace Automotive Industry Wavelet artificial neural network calculus complexity computational intelligence control convergence intelligence modeling neural network neural networks safety verification

Editors and affiliations

  • Johann Schumann
    • 1
  • Yan Liu
    • 2
  1. 1.RIACS/USRANASA Ames Research Center, M/S 269-3Moffett FieldUSA
  2. 2.Motorola Autonomic Networking LabsSchaumburgUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2010
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Print ISBN 978-3-642-10689-7
  • Online ISBN 978-3-642-10690-3
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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