By Topic

Neural network model for product end-of-life strategies

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
$33 $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

2 Author(s)
J. L. Chen ; Dept. of Mech. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan ; Jun-Nan Wu

A neural network has the advantages of ease of use and feasibility for solving nonlinear problems with learning capability. The theory of end-of-life design advisor (ELDA) is selected as the basic structure of back-propagation neural network to determine the useful strategy. Furthermore, a self organized map neural network was selected to analyze the relation between each strategy. Hence, the trained neural networks can simulate the analysis mode of ELDA rapidly and offer the designer with an easy operation method in the relative research domain.

Published in:

Electronics and the Environment, 2003. IEEE International Symposium on

Date of Conference:

19-22 May 2003