介绍了在校正大气湍流畸变波前像差的自适应光学系统中,基于神经网络技术对变形镜控制电压进行预测以减少自适应光学系统中时间延迟误差的方法。对受横向风影响的大气湍流畸变波前数据,利用数值仿真方法,研究了基于二阶学习算法的二层反向传播(BP)神经网络对自适应光学系统变形镜控制电压进行超前预测的方法,讨论了回溯帧数及学习速率对预测效果的影响,并与采用最小递归二乘(RLS)算法预测时的效果进行了比较。对比结果表明,基于二阶学习算法的二层BP神经网络预测方法比基于RLS算法的预测方法能更有效地降低系统由伺服延迟引起的误差。

To reduce the servo lag error in adaptive optics to correct the atmosphere turbulence distortion,a kind of neural network prediction algorithm to predict the control voltage of deformable mirror is proposed. The two-layer back propagation neural network prediction method with second-order learning algorithm used to predict the voltage of deformable mirror in advance is studied through numerical simulation,based on the atmospheric turbulence wavefront data influenced by transversal wind. The look-back frame and learning-rate parameter influencing the prediction effect is discussed. The residual error of the adaptive optic system is calculated with neural network prediction algorithm and recursive least-square (RLS) algorithm. The results show that the residual error caused by servo lag in the system is reduced more effectively using the neural network prediction algorithm than using the RLS prediction algorithm.