Cart (Loading....) | Create Account
Close category search window
 

A Neural Network Approach to the Validation of Simulation Models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Martens, J. ; Dept. of Appl. Econ., Leuven Catholic Univ. ; Pauwels, K. ; Put, F.

We tackle the problem of validating simulation models using neural networks. We propose a neural-network-based method that first learns key properties of the behaviour of alternative simulation models, and then classifies real system behaviour as coming from one of the models. We investigate the use of multi-layer perceptron and radial basis function networks, both of which are popular pattern classification techniques. By a computational experiment, we show that our method successfully allows to distinguish valid from invalid models for a multiserver queueing system

Published in:

Simulation Conference, 2006. WSC 06. Proceedings of the Winter

Date of Conference:

3-6 Dec. 2006

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.