A New Response Approximation Model of the Quadrant Detector Using the Optimized BP Neural Network

doi:10.1109/jsen.2019.2963050

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	A New Response Approximation Model of the Quadrant Detector Using the Optimized BP Neural Network
	Q. Li,J. B. Wu,Y. S. Chen,J. Y. Wang,S. J. Gao and Z. Y. Wu
	2020
发表期刊	Ieee Sensors Journal
ISSN	1530-437X
卷号	20 期号:8 页码:4345-4352
摘要	In this paper, a new response approximation model for quadrant detector is proposed based on a BP Neural Network by employing different training algorithms. A total of 1001 data points are gathered to train and test the proposed network. Through optimal configuration, the network with 1 hidden layer and 8 hidden neurons with Log-sigmoid transfer functions in the hidden layer is determined to have the optimum performance. Furthermore, Levenberg-Marquardt (LM) is the best train algorithm while in this case the model is more precise than others. Besides, it shows a good ability to suppress the non-uniformity. The results of experiment reveal that the root mean square error using this model is about 1/5 of that using Fusion method when the beam radius is 0.75 mm. Meanwhile, its maximum errors under different radii are all less than 6 x 10(-3) mm. Therefore, the new model would have a good application prospect in beam position measurements.
DOI	10.1109/jsen.2019.2963050
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收录类别	SCI ; EI
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/64718
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Q. Li,J. B. Wu,Y. S. Chen,J. Y. Wang,S. J. Gao and Z. Y. Wu. A New Response Approximation Model of the Quadrant Detector Using the Optimized BP Neural Network[J]. Ieee Sensors Journal,2020,20(8):4345-4352.
APA	Q. Li,J. B. Wu,Y. S. Chen,J. Y. Wang,S. J. Gao and Z. Y. Wu.(2020).A New Response Approximation Model of the Quadrant Detector Using the Optimized BP Neural Network.Ieee Sensors Journal,20(8),4345-4352.
MLA	Q. Li,J. B. Wu,Y. S. Chen,J. Y. Wang,S. J. Gao and Z. Y. Wu."A New Response Approximation Model of the Quadrant Detector Using the Optimized BP Neural Network".Ieee Sensors Journal 20.8(2020):4345-4352.

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