Using artificial neural networks to develop a mechanism for functional feature-based reference design retrieval

Y. J. Chen; Y. M. Chen; C. B. Wang; T. Chen

Using artificial neural networks to develop a mechanism for functional feature-based reference design retrieval

Y. J. Chen, Y. M. Chen, C. B. Wang, T. Chen

Institute of Manufacturing Information and Systems

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

2 Citations (Scopus)

Abstract

This study uses the adaptive resonance theory (ART) neural network to realize a mechanism for functional feature-based reference design retrieval to provide engineering designers with easy access to relevant design and related knowledge. The retrieval process includes the steps of functional feature-based query, case searching, and case ranking. The technology involves a binary code-based representation for functional features, ART neural network for functional feature-based case clustering, functional feature-based case similarity ranking, and a case-based representation for designed entities.

Original language	English
Pages	829-833
Number of pages	5
Publication status	Published - 2004
Event	Proceedings - 2004 IEEE International Engineering Management Conference: Innovation and Entrepreneurship for Sustainable Development, IEMC 2004 - , Singapore Duration: 2004 Oct 18 → 2004 Oct 21

Other

Other	Proceedings - 2004 IEEE International Engineering Management Conference: Innovation and Entrepreneurship for Sustainable Development, IEMC 2004
Country/Territory	Singapore
Period	04-10-18 → 04-10-21

All Science Journal Classification (ASJC) codes

General Engineering
Management Science and Operations Research

Cite this

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title = "Using artificial neural networks to develop a mechanism for functional feature-based reference design retrieval",

abstract = "This study uses the adaptive resonance theory (ART) neural network to realize a mechanism for functional feature-based reference design retrieval to provide engineering designers with easy access to relevant design and related knowledge. The retrieval process includes the steps of functional feature-based query, case searching, and case ranking. The technology involves a binary code-based representation for functional features, ART neural network for functional feature-based case clustering, functional feature-based case similarity ranking, and a case-based representation for designed entities.",

author = "Chen, {Y. J.} and Chen, {Y. M.} and Wang, {C. B.} and T. Chen",

year = "2004",

language = "English",

pages = "829--833",

note = "Proceedings - 2004 IEEE International Engineering Management Conference: Innovation and Entrepreneurship for Sustainable Development, IEMC 2004 ; Conference date: 18-10-2004 Through 21-10-2004",

}

Using artificial neural networks to develop a mechanism for functional feature-based reference design retrieval. / Chen, Y. J.; Chen, Y. M.; Wang, C. B. et al.
2004. 829-833 Paper presented at Proceedings - 2004 IEEE International Engineering Management Conference: Innovation and Entrepreneurship for Sustainable Development, IEMC 2004, Singapore.

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

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AU - Chen, Y. J.

AU - Chen, Y. M.

AU - Wang, C. B.

AU - Chen, T.

PY - 2004

Y1 - 2004

N2 - This study uses the adaptive resonance theory (ART) neural network to realize a mechanism for functional feature-based reference design retrieval to provide engineering designers with easy access to relevant design and related knowledge. The retrieval process includes the steps of functional feature-based query, case searching, and case ranking. The technology involves a binary code-based representation for functional features, ART neural network for functional feature-based case clustering, functional feature-based case similarity ranking, and a case-based representation for designed entities.

AB - This study uses the adaptive resonance theory (ART) neural network to realize a mechanism for functional feature-based reference design retrieval to provide engineering designers with easy access to relevant design and related knowledge. The retrieval process includes the steps of functional feature-based query, case searching, and case ranking. The technology involves a binary code-based representation for functional features, ART neural network for functional feature-based case clustering, functional feature-based case similarity ranking, and a case-based representation for designed entities.

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M3 - Paper

AN - SCOPUS:17644374208

SP - 829

EP - 833

T2 - Proceedings - 2004 IEEE International Engineering Management Conference: Innovation and Entrepreneurship for Sustainable Development, IEMC 2004

Y2 - 18 October 2004 through 21 October 2004

ER -

Using artificial neural networks to develop a mechanism for functional feature-based reference design retrieval

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All Science Journal Classification (ASJC) codes

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