Methods and Procedures for the Verification and Validation of Artificial Neural Networks

  • Brian J. Taylor

Table of contents

  1. Front Matter
    Pages i-xi
  2. Kareem Ammar, Laura Pullum, Brian J. Taylor
    Pages 13-31
  3. Laura Pullum, Brian J. Taylor
    Pages 33-49
  4. Brian J. Taylor, James T. Smith
    Pages 51-95
  5. Edgar J. Fuller, Sampath K. Yerramalla, Bojan Cukic
    Pages 97-108
  6. James T. Smith
    Pages 109-161
  7. Marjorie Darrah
    Pages 163-197
  8. Marjorie Darrah, Brian J. Taylor
    Pages 199-227
  9. Bojan Cukic, Edgar Fuller, Martin Mladenovski, Sampath Yerramalla
    Pages 257-269
  10. Back Matter
    Pages 270-277

About this book

Introduction

Artificial neural networks are a form of artificial intelligence that have the capability of learning, growing, and adapting with dynamic environments.  With the ability to learn and adapt, artificial neural networks introduce new potential solutions and approaches to some of the more challenging problems that the United States faces as it pursues the vision of space exploration and develops other system applications that must change and adapt after deployment.

Neural networks are members of a class of software that have the potential to enable intelligent computational systems capable of simulating characteristics of biological thinking and learning.  Currently no standards exist to verify and validate neural network-based systems.   NASA Independent Verification and Validation Facility has contracted the Institute for Scientific Research, Inc. to perform research on this topic and develop a comprehensive guide to performing V&V on adaptive systems, with emphasis on neural networks used in safety-critical or mission-critical applications.

Methods and Procedures for the Verification and Validation of Artificial Neural Networks is the culmination of the first steps in that research. This volume introduces some of the more promising methods and techniques used for the verification and validation (V&V) of neural networks and adaptive systems.  A comprehensive guide to performing V&V on neural network systems, aligned with the IEEE Standard for Software Verification and Validation, will follow this book. The NASA IV&V and the Institute for Scientific Research, Inc. are working to be at the forefront of software safety and assurance for neural network and adaptive systems.

Methods and Procedures for the Verification and Validation of Artificial Neural Networks is structured for research scientists and V&V practitioners in industry to assure neural network software systems for future NASA missions and other applications. This book is also suitable for graduate-level students in computer science and computer engineering.

Keywords

Adaptive systems artificial intelligence formal method learning software verification verification visualization

Authors and affiliations

  • Brian J. Taylor
    • 1
  1. 1.Institute for Scientific Research, Inc.FairmontUSA

Bibliographic information

  • DOI https://doi.org/10.1007/0-387-29485-6
  • Copyright Information Springer Science+Business Media, Inc. 2006
  • Publisher Name Springer, Boston, MA
  • eBook Packages Computer Science
  • Print ISBN 978-0-387-28288-6
  • Online ISBN 978-0-387-29485-8
  • About this book