Shape reconstruction by genetic algorithms and artificial neural networks

Liu, Xiyu, Tang, Mingxi, & Frazer, John H. (2003) Shape reconstruction by genetic algorithms and artificial neural networks. Engineering Computations, 20(2), pp. 129-151.

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This paper presents a new surface reconstruction method based on complex form functions, genetic algorithms and neural networks. Surfaces can be reconstructed in an analytical representation format. This representation is optimal in the sense of least-square fitting by predefined subsets of data points. The surface representations are achieved by evolution via repetitive application of crossover and mutation operations together with a back-propagation algorithm until a termination condition is met. The expression is finally classified into specific combinations of basic functions. The proposed method can be used for CAD model reconstruction of 3D objects and free smooth shape modelling. We have implemented the system demonstration with Visual C++ and MatLab to enable real time surface visualisation in the process of design.

Impact and interest:

6 citations in Scopus
1 citations in Web of Science®
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Full-text downloads:

1,306 since deposited on 07 Nov 2007
100 in the past twelve months

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ID Code: 10563
Item Type: Journal Article
Refereed: Yes
Keywords: Design, Genetic algorithms, Model, Neural networks
DOI: 10.1108/02644400310465281
ISSN: 0264-4401
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > TECHNOLOGY (100000)
Divisions: Past > Research Centres > CRC Construction Innovation
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2003 Emerald Publishing
Copyright Statement: Reproduced in accordance with the copyright policy of the publisher.
Deposited On: 07 Nov 2007 00:00
Last Modified: 21 Jun 2017 14:39

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