论文标题
具有显性变量的压电阶段的逆NN建模
Inverse NN Modelling of a Piezoelectric Stage with Dominant Variable
论文作者
论文摘要
本文提出了一种开发压电定位阶段的神经网络反向模型的方法，该模型表现出依赖速率的，不对称的滞后。结果表明，使用速度和加速度作为输入会导致过度拟合。为了克服这一点，得出了执行器的粗糙分析模型，并通过测量其对激发的响应，速度信号被确定为主要变量。通过将神经网络的输入空间设置为仅主要变量，可以获得具有良好预测能力的反向模型。使用Levenberg-Marquardt算法来完成网络的培训。最后，实验证明了所提出的方法的有效性。
This paper presents an approach for developing a neural network inverse model of a piezoelectric positioning stage, which exhibits rate-dependent, asymmetric hysteresis. It is shown that using both the velocity and the acceleration as inputs results in over-fitting. To overcome this, a rough analytical model of the actuator is derived and by measuring its response to excitation, the velocity signal is identified as the dominant variable. By setting the input space of the neural network to only the dominant variable, an inverse model with good predictive ability is obtained. Training of the network is accomplished using the Levenberg-Marquardt algorithm. Finally, the effectiveness of the proposed approach is experimentally demonstrated.
