Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34557
Title: Shape reconstruction by genetic algorithms and artificial neural networks
Authors: Liu XY
Tang, MX 
Frazer, JH
Keywords: Neural networks
Genetic algorithms
Model
Design
Issue Date: 2003
Publisher: Emerald Group Publishing Limited
Source: Engineering computation, 2003, v. 20, no. 2, p. 129-151 How to cite?
Journal: Engineering computation
Abstract: 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.
URI: http://hdl.handle.net/10397/34557
ISSN: 0264-4401
DOI: 10.1108/02644400310465281
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