The back-propagation(BP)neural network is proposed to correct nonlinearity and optimize the force measurement and calibration of an optical tweezer sys-tem.Considering the low convergence rate of the BP algo-rithm,the...The back-propagation(BP)neural network is proposed to correct nonlinearity and optimize the force measurement and calibration of an optical tweezer sys-tem.Considering the low convergence rate of the BP algo-rithm,the Levenberg-Marquardt(LM)algorithm is used to improve the BP network.The proposed method is experimentally studied for force calibration in a typical optical tweezer system using hydromechanics.The result shows that with the nonlinear correction using BP net-works,the range of force measurement of an optical tweezer system is enlarged by 30%and the precision is also improved compared with the polynomial fitting method.It is demonstrated that nonlinear correction by the neural network method effectively improves the per-formance of optical tweezers without adding or changing the measuring system.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.10474094).
文摘The back-propagation(BP)neural network is proposed to correct nonlinearity and optimize the force measurement and calibration of an optical tweezer sys-tem.Considering the low convergence rate of the BP algo-rithm,the Levenberg-Marquardt(LM)algorithm is used to improve the BP network.The proposed method is experimentally studied for force calibration in a typical optical tweezer system using hydromechanics.The result shows that with the nonlinear correction using BP net-works,the range of force measurement of an optical tweezer system is enlarged by 30%and the precision is also improved compared with the polynomial fitting method.It is demonstrated that nonlinear correction by the neural network method effectively improves the per-formance of optical tweezers without adding or changing the measuring system.
