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Volume 28 Issue 4
Sep.  2013
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Research of on-line measurement and non-linearity correction of two dimension PSD device

  • Received Date: 2003-08-25
    Accepted Date: 2003-12-12
  • By using a numerically controlled 2-D shifter,measurement data of PSD are obtained and then artificial neural networks are used for non-linearity correction of two dimensional PSD. When a light point moves in the detective area of PSD,the coordinates of the point at different positions are used as designed output of the networks and the output coordinates of the PSD are used as learning input of the networks,and a learning procedure is carried out. According to the ability of non-linear mapping of artificial neural networks,a linear relationship between input and output of the networks is set up after learning. Results show that learned networks can correct any non-linearity error in real time.
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  • [1] 袁红星,王志兴,贺安之.PSD非线性修正的算法研究 [J].仪器仪表学报,1999(3):16~21.

    [2] 易亚星,李忠科,邓方林.PSD精密测量中的二维表检索算法 [J].计算机测量与控制,2002(5):284~285.

    [3] 莫长涛,陈长征,张黎丽 et al.二维PSD非线性修正共轭梯度算法 [J].东北大学学报,2003,24(5):342~343.

    [4] 王爵树,张新.高线性二维光电位置传感器(PSD)研究 [J].集成电路通讯,1995(3):11~17.

    [5] HAGAN M T.Neural network design [M].北京:机械工业出版社,2002.227~255.

    [6] 王宗炎,洪振华.BP网学习算法的改进及在模式识别中的应用 [J].南京航空航天大学学报,1994(11):216~218.

    [7] NILSSON N J.Artificial intelligence anew synthesis [M].北京:机械工业出版社,1999.23~30.

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通讯作者: 陈斌, bchen63@163.com
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Research of on-line measurement and non-linearity correction of two dimension PSD device

  • 1. College of Automatic Control, Northwestern Polytechnical University, Xi'an 710072, China

Abstract: By using a numerically controlled 2-D shifter,measurement data of PSD are obtained and then artificial neural networks are used for non-linearity correction of two dimensional PSD. When a light point moves in the detective area of PSD,the coordinates of the point at different positions are used as designed output of the networks and the output coordinates of the PSD are used as learning input of the networks,and a learning procedure is carried out. According to the ability of non-linear mapping of artificial neural networks,a linear relationship between input and output of the networks is set up after learning. Results show that learned networks can correct any non-linearity error in real time.

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