天文学进展2024，Vol.42Issue(3)：519-528,10.DOI:10.3969/j.issn.1000-8349.2024.03.08
基于BP神经网络的FAST馈源舱融合测量预测研究
Research on Fusion Measurement Prediction of FAST Feed Cabin Based on BP Neural Network
摘要
Abstract
When the Five-hundred-meter Aperture Spherical Radio Telescope(FAST)per-forms the tracking observation task,for cooperating with this task,the feed has got spatial motion.The fine-tuning positioning of the feed is realized by the feed cabin,so the high-precision measurement of the position of the feed cabin is great significance.However,when the total station equipment fails,it is unable to correct the GPS/IMU fusion measurements with the Kalman algorithm,it causes the accuracy of the feed cabin measurements decreas-ing.In order to solve this problem,this paper designs a prediction model based on BP neural network,which is composed of three parts,the data preprocessing,the model design and the model training validation.And the model training data is the real measurement data of FAST with a data volume of about 40 GB.In order to verify the generalization ability of the model,three kinds of motion trajectory data are selected to test the model prediction accuracy,and the results show that the accuracy meets the 15 mm requirement under three kinds of motion trajectories.关键词
FAST/馈源舱融合测量预测/数据预处理/BP神经网络/时间序列Key words
FAST/feed cabin fusion measurement prediction/data preprocessing/BP neu-ral network/time series分类
天文与地球科学引用本文复制引用
卢朝茂,李明辉,宋本宁,彭帅,冯禹,于东俊,骆亚波..基于BP神经网络的FAST馈源舱融合测量预测研究[J].天文学进展,2024,42(3):519-528,10.基金项目
国家自然科学基金(12363010,42274055) （12363010,42274055）
贵州省科技计划项目(黔科合基础-ZK[2023]一般039,黔科合支撑[2023]一般352) （黔科合基础-ZK[2023]一般039,黔科合支撑[2023]一般352）
