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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2015

01-06-2015 | Original Article

Identification of boundary shape using a hybrid approach

Authors: Na Tian, Longchao Zhu, Choi-Hong Lai

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2015

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Abstract

In this paper, a hybrid approach combining quantum-behaved particle swarm optimization (QPSO) and conjugate gradient method is proposed to identify boundary shape of the geometry under steady state conditions. No prior information about the shape is available, so the inverse problem is classified as function estimation. Least square method is used to model the inverse problem, which intends to minimize the difference between measured and calculated data. Considering ill-posedness of the inverse problem, Tikhonov regularization method is used to stabilize the solution. The numerical results show that the proposed hybrid method is able to recover the boundary shape, and can sharply reduce the required computation time. While considering the oscillations at the both boundaries of the estimated results, the parallel QPSO is used in order to both obtain better estimation and reduce computation time.
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Metadata
Title
Identification of boundary shape using a hybrid approach
Authors
Na Tian
Longchao Zhu
Choi-Hong Lai
Publication date
01-06-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2015
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0266-9

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