Guidance for the verification and validation of neural networks / (Record no. 74425)

000 -LEADER
fixed length control field 04103nam a2200589 i 4500
001 - CONTROL NUMBER
control field 7304003
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220712205923.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 151222s2015 njua ob 001 eng d
015 ## - NATIONAL BIBLIOGRAPHY NUMBER
-- GBA874568 (print)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9781119134671
-- electronic
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 047008457X
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
-- print
082 04 - CLASSIFICATION NUMBER
Call Number 006.32
100 1# - AUTHOR NAME
Author Pullum, Laura L.,
245 10 - TITLE STATEMENT
Title Guidance for the verification and validation of neural networks /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 PDF (ix, 133 pages) :
490 1# - SERIES STATEMENT
Series statement Emerging technologies
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Areas of consideration for adaptive systems -- Verification and validation of neural networks-guidance -- Recent changes to IEEE std 1012.
520 ## - SUMMARY, ETC.
Summary, etc Guidance for the Verification and Validation of Neural Networks is a supplement to the IEEE Standard for Software Verification and Validation, IEEE Std 1012-1998. Born out of a need by the National Aeronautics and Space Administration's safety- and mission-critical research, this book compiles over five years of applied research and development efforts. It is intended to assist the performance of verification and validation (V&V) activities on adaptive software systems, with emphasis given to neural network systems. The book discusses some of the difficulties with trying to assure adaptive systems in general, presents techniques and advice for the V&V practitioner confronted with such a task, and based on a neural network case study, identifies specific tasking and recommendations for the V&V of neural network systems. "As the demand for developing and assuring adaptive systems grows, this guidebook will provide practitioners with the insight and practical steps for verifying and validating neural networks. The work of the authors is a great step forward, offering a level of practical experience and advice for the software developers, assurance personnel, and those performing verification and validation of adaptive systems. This guide makes possible the daunting task of assuring this new technology. NASA is proud to sponsor such a realistic approach to what many might think a very futuristic subject. But adaptive systems with neural networks are here today and as the NASA Manager for Software Assurance and Safety, I believe this work by the authors will be a great resource for the systems we are building today and into tomorrow." -Martha S. Wetherholt, NASA Manager of Software Assurance and Software Safety NASA Headquarters, Office of Safety & Mission Assurance.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
Subject Neural networks (Computer science)
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
Subject Computer programs
General subdivision Validation.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
Subject Computer programs
General subdivision Verification.
700 1# - AUTHOR 2
Author 2 Taylor, Brian J.
700 1# - AUTHOR 2
Author 2 Darrah, Majorie A.
856 42 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=7304003
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Hoboken, New Jersey :
-- IEEE Computer Society,
-- c2007.
264 #2 -
-- [Piscataqay, New Jersey] :
-- IEEE Xplore,
-- [2015]
336 ## -
-- text
-- rdacontent
337 ## -
-- electronic
-- isbdmedia
338 ## -
-- online resource
-- rdacarrier
588 ## -
-- Description based on PDF viewed 12/22/2015.

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