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Optical Engineering

Model of an adaptive optical system controlled by a neural network
Author(s): Gleb V. Vdovin
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Paper Abstract

An adaptive optical system (AOS) with a feedback loop closed via a feedforward neural network (NN) is considered. The vector of the wavefront corrector control signals is computed by the network from two vectors of the intensity moments measured in two near-field planes by two matrix photodetectors. The NN is trained with a back-propagation algorithm to predict the vector of adaptative mirror signals from the measured intensity vectors. During training, the network forms an optimal control algorithm for a given configuration of an optical system, taking into account misalignments and nonlinearities of the hardware used. A numerical model of a multichannel AOS controlled by a multilayer NN was built, trained, and run for difterent input aberrations. The neural control permits a direct conversion of the intensity distribution measured in the near field into control signals of a wavefront corrector. High efficiency of control has been demonstrated for a model of a 16-channel adaptive optical system for arbitrary input aberrations having a limited spatial spectrum.

Paper Details

Date Published: 1 November 1995
PDF: 5 pages
Opt. Eng. 34(11) doi: 10.1117/12.212907
Published in: Optical Engineering Volume 34, Issue 11
Show Author Affiliations
Gleb V. Vdovin, Delft Univ. of Technology (Netherlands)


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