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Parametric design by learning

Authors: Chen, YH;

Parametric design by learning

Abstract

Parametric design is an effective and productive tool for the definition and modification of geometric models. This paper presents an intelligent method for the creation of a parametric model. In the proposed method, an adaptive neural network model is built to map the set of dimensional parameters to a set of coordinates. Defining a parametric model is equivalent to teaching the neural network. The user needs only specify a set of dimensional parameters that defines the parametric model and teach the neural network how to react to the changes of the dimensional parameters. Once the neural net work is taught, any dimensional changes will result in corresponding coordinate changes. This novel method eliminate the need of programming or graphic interaction that are normally required by contemporary parametric design systems.

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China (People's Republic of)
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Average
Average
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