Neural network model for product end-of-life strategies

Jahau Lewis Chen, Jun Nan Wu

Research output: Contribution to conference › Paper

2 Citations (Scopus)

Abstract

Neural network has the advantages of easily to use and feasible to non-linear 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, self organize map neural network was selected to analyze the relation between each strategies. 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.

Original languageEnglish
Pages159-164
Number of pages6
Publication statusPublished - 2003 Jul 17
Event2003 IEEE International Symposium on Electronics and the Environment - Boston MA, United States
Duration: 2003 May 19 → 2003 May 22

Other

Other2003 IEEE International Symposium on Electronics and the Environment
CountryUnited States
CityBoston MA
Period03-05-19 → 03-05-22

Fingerprint

Neural networks
back propagation
learning
Backpropagation
product
analysis
method

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Waste Management and Disposal
  • Pollution
  • Electrical and Electronic Engineering

Cite this

Chen, J. L., & Wu, J. N. (2003). Neural network model for product end-of-life strategies. 159-164. Paper presented at 2003 IEEE International Symposium on Electronics and the Environment, Boston MA, United States.
Chen, Jahau Lewis ; Wu, Jun Nan. / Neural network model for product end-of-life strategies. Paper presented at 2003 IEEE International Symposium on Electronics and the Environment, Boston MA, United States.6 p.
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Chen, JL & Wu, JN 2003, 'Neural network model for product end-of-life strategies' Paper presented at 2003 IEEE International Symposium on Electronics and the Environment, Boston MA, United States, 03-05-19 - 03-05-22, pp. 159-164.

Neural network model for product end-of-life strategies. / Chen, Jahau Lewis; Wu, Jun Nan.

2003. 159-164 Paper presented at 2003 IEEE International Symposium on Electronics and the Environment, Boston MA, United States.

Research output: Contribution to conference › Paper

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T1 - Neural network model for product end-of-life strategies

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AU - Wu, Jun Nan

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N2 - Neural network has the advantages of easily to use and feasible to non-linear 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, self organize map neural network was selected to analyze the relation between each strategies. 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.

AB - Neural network has the advantages of easily to use and feasible to non-linear 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, self organize map neural network was selected to analyze the relation between each strategies. 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.

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Chen JL, Wu JN. Neural network model for product end-of-life strategies. 2003. Paper presented at 2003 IEEE International Symposium on Electronics and the Environment, Boston MA, United States.

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