The longitudinal distortion , lateral displacement and luminance difference of the reconstructed images at different depth of field in multiple - exposure rainbow holography for 3 - d synthesis of multiple ct slides were analyzed , for which the effective compensation methods were presented , and the experimental results were given 分析了应用彩虹全息术实现ct图片的立体合成过程中再现像在轴向及垂轴方向上的畸变以及再现像亮度的失真,分别提出了相应的补偿方法，并给出了相应的实验结果
As neural network has the ability of self - learning , that utilizes prior output data of uncertain system to estimate iteratively the static state property of system in order to achieve ideal approaching precision for identification of the positive model , a robust iterative learning control scheme on the basis of better positive model is designed . the neural network is used to identify the positive model of nonlinear system on iterative axis , which can give feed - forward action of iterative learning controller to reduce the effects of nonlinear properties and model uncertainties . meanwhile , feedback action of iterative learning controller make joint movement follow the desired trajectory on time axis by using controlled parameters derived by the neural network 由于神经网络具有自学习能力，它可利用不确定性系统的历史输出数据对系统的稳态特性进行估计，使得对系统正向模型的辨识达到理想的逼近精度，然后在此正向模型的基础上进行学习控制律的设计：即采用神经网络辨识非线性系统的正向模型，并消除系统不确定性和外部干扰的影响，使关节运动沿迭代轴方向逼近期望轨迹；迭代学习控制器在线学习控制参量，使关节运动沿时间轴方向跟踪期望轨迹。
