ISSN:
1013-9826
Source:
Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Aiming at hole filling in points cloud data reconstruction, a novel neural networkarithmetic was employed in abridged points cloud data surface reconstruction. Radial basis functionneural network and simulated annealing arithmetic was combined. Global optimization feature ofsimulated annealing was employed to adjust the network weights, the arithmetic can keep thenetwork from getting into local minimum. MATLAB program was compiled, experiments onabridged points cloud data have been done employing this arithmetic, the result shows that thisarithmetic can efficiently approach the surface with 10-4 mm error precision, and also the learningspeed is quick and hole filling algorithm is successful and the reconstruction surface is smooth.Different methods have been employed to do surface reconstruction in comparison, the resultsillustrate the error employed algorithmic proposed in the paper is little and converge speed is quick
Type of Medium:
Electronic Resource
URL:
http://www.tib-hannover.de/fulltexts/2011/0528/01/57/transtech_doi~10.4028%252Fwww.scientific.net%252FKEM.392-394.750.pdf
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