Space Camera Focusing Forecast Based on RBF Network

被引：0
|
作者：
Wu, Xingxing [1 ]
Liu, Jinguo [1 ]
Yu, Da [1 ]
机构：
[1] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130033, Jilin, Peoples R China
来源：
COMPUTATIONAL INTELLIGENCE AND INTELLIGENT SYSTEMS | 2009年 / 51卷
关键词：
Space camera; Neural network; Radial basis function; Focusing;
D O I：
10.1007/978-3-642-04962-0_23
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
As circumstance temperature of space camera changes, flex of structural components and distortion of optical components lead to change of focal length and image quality. Radial Basis Function (RBF) network is used to approximate the complex nonlinear relation between focalization quantity, image quality, temperature level and axial temperature difference of space camera. After the RBF Network is trained with thermo-optical experiment data, temperature level and axial temperature difference could be input to the network to obtain colder value of best image position. In this way focusing forecast under different temperatures can be realized. Results of focusing forecast experiment validate this method.
引用
收藏
页码：199 / 206
页数：8
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