Application of BP Neural Networks in Non—linearity Correction of optical tweezers

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Application of BP Neural Networks in Non—linearity Correction of optical tweezers

Article | 更新时间：2020-08-12

- Application of BP Neural Networks in Non—linearity Correction of optical tweezers
- Optics and Precision Engineering Vol. 16, Issue 1, Pages: 6-10(2008)
- 作者机构：
  
  中国科学技术大学物理系
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Received：13 April 2007，
  
  Revised：05 July 2007，
  
  Published Online：22 January 2008，
  
  Published：22 January 2008
- 稿件说明：
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王自强,李银妹,楼立人,魏衡华,王忠. BP神经网络用于光镊力的非线性修正研究[J]. 光学精密工程, 2008,16(1):6-10

Application of BP Neural Networks in Non—linearity Correction of optical tweezers[J]. Optics and precision engineering, 2008, 16(1): 6-10.
王自强,李银妹,楼立人,魏衡华,王忠. BP神经网络用于光镊力的非线性修正研究[J]. 光学精密工程, 2008,16(1):6-10 DOI：

Application of BP Neural Networks in Non—linearity Correction of optical tweezers[J]. Optics and precision engineering, 2008, 16(1): 6-10. DOI：

摘要

光镊是测量微小力的有效工具，力的测量范围和测量精度是光镊系统的重要指标。本研究提出运用BP神经网络方法对光镊系统的力的测量和标定进行非线性修正，并通过实际应用检验，证明BP神经网络方法对光镊系统力的测量进行非线性修正，不仅扩大了光镊系统力的测量范围，而且与多项式拟合方法相比，该方法的精度更高，从而有效提高了光镊系统的性能指标。

Abstract

Optical tweezers is the probe studied weak force measurement， Measurement Range and Measurement Accuracy of force are important index of the system of optical tweezers. BP optimizations for neural networks to correct the non—linearity of calibration of the force of optical trap are provided in this paper． The range of measurement of the system of optical tweezers of data is improved without increasing the complexity of hardware. With the comparison of BP method and curve fitting method

it is presented that the nonlinear correction method based on neural network has high measurement precision. Experimental results of Nano-Optical-Tweezers system show that in such a way

the performance of the system is effectively improved.

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