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Proceedings Paper

Glass optimization using neural network
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Paper Abstract

The possibility of using neural network to handle discrete variables (glass materials) in lens design is investigated. First, a two-dimensional neuron array is established, in which the minimum of the network energy function corresponds to a design result with controlled chromatic aberrations, acceptable monochromatic aberrations and with a proper combination of selected real glasses. The values of connection matrix and the bias currents are then calculated by means of ray tracing. They are applied to update the neuron asynchronously and randomly, until the valid solutions are achieved. 21 recommended Chinese optical glasses are selected to form a small catalog for the neural network model to reduce the number of the neurons and increase the convergence rate of optimization. A test program is developed using the Macro-PLUS language in CODE V and a double Gauss camera lens is successfully optimized with the model.

Paper Details

Date Published: 10 February 2005
PDF: 6 pages
Proc. SPIE 5638, Optical Design and Testing II, (10 February 2005); doi: 10.1117/12.579821
Show Author Affiliations
Xuemin Cheng, Beijing Institute of Technology (China)
Yongtian Wang, Beijing Institute of Technology (China)
Qun Hao, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 5638:
Optical Design and Testing II
Yongtian Wang; Zhicheng Weng; Shenghua Ye; Jose M. Sasian, Editor(s)

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