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A neural network-based machine learning approach for supporting synthesis

Published online by Cambridge University Press:  27 February 2009

Nenad Ivezic  and
James H. Garrett Jr
Nenad Ivezic
Affiliation:
Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, PA 15213–3890
James H. Garrett Jr
Affiliation:
Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, PA 15213–3890
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Abstract

The goal of machine learning for artifact synthesis is the acquisition of the relationships among form, function, and behavior properties that can be used to determine more directly form attributes that satisfy design requirements. The proposed approach to synthesis knowledge acquisition and use (SKAU) described in this paper, called NETSYN, creates a function to estimate the probability of each possible value of each design property being used in a given design context. NETSYN uses a connectionist learning approach to acquire and represent this probability estimation function and exhibits good performance when tested on an artificial design problem. This paper presents the NETSYN approach for SKAU, a preliminary test of its capability, and a discussion of issues that need to be addressed in future work.

Keywords

Design SynthesisMachine LearningNeural Networks
Type
Articles
Information
AI EDAM , Volume 8 , Issue 2 , Spring 1994 , pp. 143 - 161
Copyright
Copyright © Cambridge University Press 1994

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