Modeling for tracking error of theodolite based on RBF neural network

LI Miao; GAO Hui-bin

doi:10.3788/OPE.20122004.0818

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Modeling for tracking error of theodolite based on RBF neural network

Article | 更新时间：2020-08-12

- Modeling for tracking error of theodolite based on RBF neural network
- Optics and Precision Engineering Vol. 20, Issue 4, Pages: 818-825(2012)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院研究生院北京,100039
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122004.0818
  CLC： V556;TP183
- Received：04 May 2011，
  
  Revised：26 May 2011，
  
  Published Online：22 April 2012，
  
  Published：22 April 2012
- 稿件说明：
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李淼, 高慧斌. 应用径向基函数神经网络的经纬仪跟踪误差建模[J]. 光学精密工程, 2012,(4): 818-825

LI Miao, GAO Hui-bin. Modeling for tracking error of theodolite based on RBF neural network[J]. Editorial Office of Optics and Precision Engineering, 2012,(4): 818-825
李淼, 高慧斌. 应用径向基函数神经网络的经纬仪跟踪误差建模[J]. 光学精密工程, 2012,(4): 818-825 DOI： 10.3788/OPE.20122004.0818.

LI Miao, GAO Hui-bin. Modeling for tracking error of theodolite based on RBF neural network[J]. Editorial Office of Optics and Precision Engineering, 2012,(4): 818-825 DOI： 10.3788/OPE.20122004.0818.

摘要

提出了一种基于径向基函数(RBF)神经网络建立光电经纬仪等效跟踪误差模型的方法来评价光电经纬仪的跟踪性能。分析了光电经纬仪存在的非线性因素

说明了采用理论建模方法难以准确描述其全部过程的原因。然后

介绍了RBF神经网络和靶标系统

基于一组靶标参数建立了RBF神经网络模型

并更换靶标参数进行模型验证。最后

对更换后的靶标参数进行重新训练建模

并改变参数周期

对模型进行了验证。实验结果表明:所建的神经网络模型精度与靶标参数有关

当动态靶标的半椎角

为21.2

倾角

为43.8

靶标匀速运行周期

为8.5 s时

网络模型在靶标速度最大时误差也达到最大为3.18'

其它时刻均小于0.6'。当

为16.6

为37.5

为13 s时

模型最大误差为1.8'左右

在此模型下真实输出与网络模型输出的最大偏差为2.4'左右

其它时刻均小于1.2'。结果表明

采用RBF神经网络所建立的跟踪误差模型能够反应真实系统的情况

是可行实用的

且具有较高的精度和泛化能力。

Abstract

To effectively evaluate the tracking ability of a photoelectric theodolite

a new tracking error model based on the Radial Basis Function(RBF) neural network was established. First

the nonlinear factors existing in the theodolite were described and the reason why the system was hard to be modeled based on theory was discussed. Then

the RBF neural network theory and the target system were introduced

and the RBF neural network model was built and verified in different parameters. Finally

the network model with new parameters and data was trained and the new network model was obtained through changing parameter periods. Experimental results indicate that the precision of the neural network is closely dependent on the target system parameters. When the half cone angle(

) and the tilt angle(

) of a dynamic target are 21.2? and 43.8?

respectively

and the moving period(

) is 8.5 s

the maximum model error is 3.18' in the acceleration coming to the maximum. And for other time

the model error is less than 0.6'. Furthermore

when the

and

are 16.6?

37.5?

and

is 13 s

the maximum model error is about 1.8'. With the network model

the maximum error between model output and real output is 2.4' in the speed coming to maximum. And for other time

the maximum model error is less than 1.2'. The results indicate that the network model based on RBF neural network can replace a real system in a certain sense. It is feasible and has high accuracy and important value to the engineering practice.

关键词

Keywords

references

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