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Optical Engineering

of ion exchange parameters by a neural network based on particle swarm optimization
Author(s): Jing Yuan; Fengguang Luo; Liang Gao; Chi Zhou; Wanjun Chen; Bin Zhang
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Paper Abstract

Modeling the process of ion exchange in glass requires accurate knowledge of the self-diffusion coefficients of the incoming and outgoing ions. Furthermore, correlating the concentration profile of the incoming ions to a change in refractive index requires knowledge of the correlation coefficient. A novel method of a neural network based on a particle swarm optimization algorithm is considered. In the range of training, the performance parameters of ion-exchanged waveguides in any arbitrary experiment condition can be obtained easily and quickly. This method has the advantages of reliability, accuracy, and time efficiency, which are identified by simulation. Therefore, it has promise in both fields of investigation and applications.

Paper Details

Date Published: 1 February 2008
PDF: 6 pages
Opt. Eng. 47(2) 024601 doi: 10.1117/1.2870091
Published in: Optical Engineering Volume 47, Issue 2
Show Author Affiliations
Jing Yuan, Huazhong Univ. of Science and Technology (China)
Fengguang Luo, Huazhong Univ. of Science and Technology (China)
Liang Gao, Huazhong Univ. of Science and Technology (China)
Chi Zhou, Huazhong Univ. of Science and Technology (China)
Wanjun Chen, Huazhong Univ. of Science and Technology (China)
Bin Zhang, Huazhong Univ. of Science and Technology (China)


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