Neural network model for product end-of-life strategies

Jahau Lewis Chen; Jun Nan Wu

Neural network model for product end-of-life strategies

Jahau Lewis Chen, Jun Nan Wu

Department of Mechanical Engineering

Research output: Contribution to conference › Paper › peer-review

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 language	English
Pages	159-164
Number of pages	6
Publication status	Published - 2003 Jul 17
Event	2003 IEEE International Symposium on Electronics and the Environment - Boston MA, United States Duration: 2003 May 19 → 2003 May 22

Other

Other	2003 IEEE International Symposium on Electronics and the Environment
Country	United States
City	Boston MA
Period	03-05-19 → 03-05-22

All Science Journal Classification (ASJC) codes

Environmental Engineering
Waste Management and Disposal
Pollution
Electrical and Electronic Engineering

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title = "Neural network model for product end-of-life strategies",

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.",

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