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融合遗传算法和BP神经网络的光斑定位方法

张景源 陈北北 杨永兴 朱庆生 李金鹏 赵金标

张景源, 陈北北, 杨永兴, 朱庆生, 李金鹏, 赵金标. 融合遗传算法和BP神经网络的光斑定位方法[J]. 中国光学(中英文). doi: 10.37188/CO.2022-0084
引用本文: 张景源, 陈北北, 杨永兴, 朱庆生, 李金鹏, 赵金标. 融合遗传算法和BP神经网络的光斑定位方法[J]. 中国光学(中英文). doi: 10.37188/CO.2022-0084
ZHANG Jing-yuan, CHEN Bei-bei, YANG Yong-xing, ZHU Qing-sheng, LI Jin-peng, ZHAO Jin-biao. Positioning algorithm for laser spot center based on BP neural network and genetic algorithm[J]. Chinese Optics. doi: 10.37188/CO.2022-0084
Citation: ZHANG Jing-yuan, CHEN Bei-bei, YANG Yong-xing, ZHU Qing-sheng, LI Jin-peng, ZHAO Jin-biao. Positioning algorithm for laser spot center based on BP neural network and genetic algorithm[J]. Chinese Optics. doi: 10.37188/CO.2022-0084

融合遗传算法和BP神经网络的光斑定位方法

doi: 10.37188/CO.2022-0084
基金项目: 国家自然科学基金(No.12003067)
详细信息
    作者简介:

    张景源(1997—),男,河南商丘人,硕士研究生在读,2019年于东南大学获得学士学位,现于中国科学技术大学攻读硕士学位,主要从事天文仪器的计算机控制系统及图像处理方面的研究。E-mail:jyzhangx@mail.ustc.edu.cn

    朱庆生(1969—),男,江苏连云港人,研究员,硕士生导师,1992年于南京大学获得学士学位,主要从事天文仪器的软件系统设计方面的研究。E-mail:85482014@163.com

  • 中图分类号: TP249

Positioning algorithm for laser spot center based on BP neural network and genetic algorithm

Funds: Supported by National Natural Science Foundation of China (No.12003067)
  • 摘要:

    针对振动环境中传统光斑中心定位算法存在的处理时间长、精度低等问题,本文提出一种基于遗传算法优化BP神经网络的光斑定位方法。算法使用BP神经网络对光斑位置进行预测,并通过遗传算法对神经网络进行优化。构建BP神经网络模型,将使用质心、形心、高斯拟合等方法求出的光斑中心位置以及形心法求出的光斑半径作为输入,对光斑真实中心位置进行预测。并使用遗传算法优化神经网络的权值和阈值,以增强预测效果。搭建实验环境,通过对光学系统外加干扰模拟振动环境,采集数据用于神经网络训练和算法验证。实验结果表明,优化前后的标定测试迭代次数分别为55和29,平均误差分别为0.81像素和0.45像素。在遗传算法的优化下,神经网络算法迭代速度和预测精度均有所提高。

     

  • 图 1  遗传算法优化BP神经网络流程图

    Figure 1.  Flow chart of genetic algorithm combined with BP neural network

    图 2  BP神经网络结构

    Figure 2.  Structure of BP neural network

    图 3  神经网络模型

    Figure 3.  Structure of the neural network model

    图 4  实验硬件平台

    Figure 4.  Experimental hardware platform

    图 5  光斑x轴抖动情况

    Figure 5.  x-axis coordinates of the spot center

    图 6  激光光斑图像

    Figure 6.  Laser Spot image

    图 7  BP神经网络训练曲线

    Figure 7.  Training curve of BP neural network

    图 8  GA-BP神经网络训练曲线

    Figure 8.  Training curve of GA-BP neural network

    图 9  两种网络进行预测

    Figure 9.  Comparison of two network predictions

    图 10  进行控制后光斑x轴变化情况

    Figure 10.  x-axis coordinates of spot center when under control

    表  1  训练数据

    Table  1.   Training data

    Grayscale
    centering
    centroidGaussian
    fitting
    radiusActual
    Coordinate
    (252.30,
    305.11)
    (255.43,
    309.27)
    (254.17,
    307,91)
    7(253.30,
    307,11)
    下载: 导出CSV

    表  2  两种网络性能对比

    Table  2.   Performance comparison of the two networks

    Neural NetworkBPGA-BP
    Number of iterations5529
    Mean error/pixel0.760.42
    下载: 导出CSV

    表  3  两种网络性能测试

    Table  3.   Performance test of the two networks

    Neural NetworkBPGA-BP
    Number of iterations4732
    Mean error/pixel1.210.73
    下载: 导出CSV
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  • 录用日期:  2022-08-24
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