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.
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.
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|Item Type:||Journal Article|
|Keywords:||Design, Genetic algorithms, Model, Neural networks|
|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:||Current > Research Centres > CRC Construction Innovation
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Design
|Copyright Owner:||Copyright 2003 Emerald Publishing|
|Copyright Statement:||Reproduced in accordance with the copyright policy of the publisher.|
|Deposited On:||07 Nov 2007|
|Last Modified:||29 Feb 2012 13:53|
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