BP神经网络用于光镊力的非线性修正研究

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。

Application of BP neural network to nonlinearity correction of optical tweezer force

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.