您当前的位置：
首页 >
文章列表页 >
Compensation and correction of segmented telescopes based on multilayer perceptron neural network
Information Sciences | 更新时间：2026-03-12

    • Compensation and correction of segmented telescopes based on multilayer perceptron neural network

    • “Experts proposed a splicing telescope compensation and correction method based on MLP neural network, verified that component eccentricity and tilt can be compensated for each other, established an optical system model, trained subsystem models, and experiments showed that this method can effectively predict and adjust the compensation amount, reduce the sub mirror adjustment dimension, and provide a new approach for high-precision adjustment.”
    • Optics and Precision Engineering   Vol. 34, Issue 5, Pages: 816-829(2026)

    • DOI：10.37188/OPE.20263405.0816    

      CLC： TH751;TH743
    • CSTR：32169.14.OPE.20263405.0816    

    • Received：23 December 2025，

      Revised：2026-01-12，

      Published：10 March 2026

    移动端阅览

  • ZHU Bangkun,WANG Kun,XU Boqian,et al.Compensation and correction of segmented telescopes based on multilayer perceptron neural network[J].Optics and Precision Engineering,2026,34(05):816-829.

  •  
  •  

0

Views

0

下载量

0

CSCD

Alert me when the article has been cited
提交
Tools
Download
Export Citation
Share
Add to favorites
Add to my album

Related Articles

Physics-guided deep neural network for TDI CCD noise modeling
Centroid localization of dynamic star sensors under proton irradiation
Full-stokes photodetector based on neural networks
Detection of conductive multi-particles based on circular convolutional neural network
Partial optimal transport-based domain adaptation for hyperspectral image classification

Related Author

XIA Bo
HUANG Hong
ZHOU Jianyong
YANG Liping
WANG Tao
LUO Zhiwen
FENG Jie
GUO Qi

Related Institution

The 44th Research Institute of China Electronics Technology Group Corporation
Key Laboratory of Optoelectronic Technology and Systems of the Education Ministry of China， Chongqing University
State Key Laboratory of Functional Materials and Devices for Special Environmental Conditions
Xinjiang Key Laboratory of Extreme Environment Electronics
University of Chinese Academy of Sciences
0