Shape reconstruction by genetic algorithms and artificial neural networks
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Description
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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ID Code: | 10563 |
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Item Type: | Contribution to Journal (Journal Article) |
Refereed: | Yes |
Measurements or Duration: | 23 pages |
Keywords: | Neural networks, design, genetic algorithms, model |
DOI: | 10.1108/02644400310465281 |
ISSN: | 0264-4401 |
Pure ID: | 34141137 |
Divisions: | Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering |
Copyright Owner: | Consult author(s) regarding copyright matters |
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au |
Deposited On: | 07 Nov 2007 00:00 |
Last Modified: | 03 Mar 2024 16:46 |
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